Cognitive Neural Mechanism of Sports Competition Pressure Source

Abstract At sports events, the athletes by the pressure source is varied, based on the stress status of athletes, many athletes stress related experts at home and abroad to design questionnaire, questionnaire and sports events for athletes with often life process of in-depth and meticulous investigation, has formed the one whole set athletes pressure source of cognitive neuroscience assessment system, sports competition for athlete’s "escort". By participating in state general administration of sports scientific research project " management system of athlete competition pressure cognitive neuroscience" the development of using psychological pressure on athletes’ source data, the application of natural language processing and machine learning technology research these data, mainly using clustering algorithm and recommendation algorithm, thus forming pressure source research results are applied in sports competitions.


Introduction
Competition pressure is a special type of pressure which is mainly generated in the competition environment. Sports competition itself is very challenging, so it is inevitable for athletes to be in a state of stress for a long time [1]. General psychology and sport psychology research shows that competition pressure will give individual athletes and sports organizations to bring many negative effects, such as to cause anxiety, aggressive behavior, low satisfaction, and thus affect the competition results and health of body and mind. Poor coping skills will increase the muscle tension of the athlete, making the player's attention and performance significantly lower [2]. Understand the players before the game after the source of stress and stress level is very necessary, and athletes only to fully understand and know their own stress factors, adjust their psychological state, will win the game in the arena [3,4].
In order to enable the athletes to play their competitive level reasonably, psychological professionals must make regular psychological counseling and psychological diagnosis for the athletes. Athletes psychological status, however, usually has the uncertainty and fuzziness, and most of the psychological service is to provide some simple corresponding method, unable to cope with cross complex actual condition, and with the increased number of athletes, psychological professionals can't effectively consider the psychological status of each person [5]. How to effectively provide individualized service for individual athletes is a difficult problem.
Traditional psychological counseling is usually conducted by psychological professionals, or some questionnaires are given to measure the psychological status of athletes. This article put forward the clustering algorithm and based on the content recommendation algorithm is applied to race, stress analysis and recommendations are given after clustering corresponding coping strategies, athlete pressure makes athlete facing pressure to improve psychological condition of the athletes is more compressive ability, bear setbacks difficulties psychological level be improved.
In this paper, by participating in state general administration of sports scientific research project "management system of athlete competition pressure cognitive neuroscience" the development of using psychological pressure on athletes' source data, the application of natural language processing and machine learning technology research these data, mainly using clustering algorithm and recommendation algorithm, thus forming pressure source research results are applied in sports competitions.

Cognitive neuroscience
The study of cognitive neuroscience aims to elucidation the brain mechanism of cognitive activities, that is, how the human brain USES its components at all levels, including molecules, cells, brain tissue areas and the whole brain to realize various cognitive activities [6]. The study of cognitive development is no exception [7,8]. Since cognitive developmental psychology and developmental neuroscience are interested in many common problems, the derived developmental cognitive neuroscience is attracting more and more attention and has become one of the hottest cross-disciplinary research fields.
Competitive stressors refer to "environmental needs directly related to competition". How to systematically summarize and sort out these needs has become the primary task of competitive stressors research. According to the specific sources of environmental needs, it can be divided into organizational pressure source and competitive pressure source. 14 elite athletes from different sports organizations were studied, and the results showed that the subjects faced a lot of competitive pressure sources and organizational pressure sources.
Using code analysis, the researchers will be competitive pressure source divided into seven aspects: preparation, such as inadequate physical preparation, injury (such as the opponent's flagrant foul), pressure (such as the result of the game directly decides the personnel selection), competitors (if you don't understand the opponent), self (e.g., in the game to show the perfect shape) and events (e.g., performing complex technical movements), superstition, such as not take "lucky" equipment to match).
And organizational pressure source is classified as four aspects: environmental issues, such as personnel selection unfair, the lack of financial support, training, facilities), personal problems, such as to his own expectations too high, the lack of goals and direction, and state for their high hopes), leading questions, such as tension coaches and athletes, coaches are not good at communication, coach too bossy, the coach's professional skill not good), team issues (such as the lack of communication between teammates, his teammates adventurous, management did not fulfil his obligations, its role is not clear). The study also found that athletes reported more accurate amounts of organizational stressors and more diverse forms of performance than competitive stressors.

K-means clustering
Clustering is unsupervised learning that groups similar objects into a cluster. The clustering method can be applied to almost all objects.
The more similar the objects in the cluster are, the better the clustering effect will be. K in the k-means algorithm represents the clustering of K clusters. Means to take the mean of the data values in each cluster as the centre of the cluster, or the centre of mass. That is to say, the centre of mass of each class is used to describe the cluster. The biggest difference between clustering and classification is that the goal of classification is known in advance, while clustering is different. Clustering does not know what the target variable is in advance, and categories are not defined in advance like classification. Therefore, clustering is sometimes called unsupervised learning.
Cluster analysis tries to group similar objects into the same cluster and classify dissimilar objects into different clusters. Therefore, it is obviously necessary to find an appropriate similarity calculation method. There are many known similarity calculation methods, such as Euclidean distance, cosine distance, hamming distance and so on. In fact, we should select the appropriate similarity calculation method according to the specific application.
It is widely used as the k-means algorithm and is sometimes referred to as the Lloyd algorithm (especially in computer science) [9,10]. Given the initial k mean points (1) (1) false, the algorithm follows the following two steps:

I) Assignment:
Each observation was assigned to the cluster to minimize the sum of squares and (WCSS) in the group.
Each p x false is assigned to a defined cluster t S false, although in theory it may be allocated to two or more clusters.

II) Update:
For each cluster obtained in the previous step, the center of the observed value in the clustering is used as the new mean point.
Because the arithmetic average is the least

Application of K-means in stress analysis
With the rise of sports organizational behaviour, organizational stress has become a research hotspot. It breaks through the one-

I) Data for clustering
In chapter 3, the questionnaire is divided into: competition pressure source text, but this is not the final result, we can start with hierarchical clustering algorithm for clustering, and based on the improved algorithm, and calculate the next K-means the initial clustering center, convenient for subsequent calculations [11,12].
After calculation, the initial clustering center can be obtained as shown in table 5:

II) Result analysis
Improved hierarchical K -Means algorithm to cluster the data sets, the source of competition pressure, social support, the athlete burnout 22 dimensions such as clustering, get the final clustering center as shown in table 5.
In figure 1 and figure 2 it can be seen that the first class occupies the most (blue line);    Figure 2. The pressure distribution of layered algorithm in sports competition among them, the sense of control score is low, this is because the project is a reverse score, score is low, explain athlete mindset is good, the questionnaire shows that control aspect mainly refers to the athletes to their movement status, mindset rated items; Group 2 athletes compared with 1 class athletes, in terms of low, the pressure source does not conform to, less pressure).

Conclusions
Group 1 athletes, in general is good in toughness, but control is good in toughness, can maintain a good degree of enthusiasm [6]; In respect of competition pressure source, can see clearly that class 3 athlete's greatest pressure source in the selection, followed by the competition pressure goals: finally, in the athlete's burnout scale, negative evaluation scores highest motion, this shows that the third class athletes is easy to produce resistance to movement of sports question, lack of concentration, and then produce resistance.
The next step will be to integrate the results from neuropsychological, brain injury and functional imaging studies with the help of decision-making evaluation options, implementation of selection actions, and threestage models of decision-making outcomes and learning processes in decision-making.
Looking forward to is the future research direction