Abstract: For that texts in power audit field has features with obvious industry characteristics，high text feature similarity，and fuzzy classification boundaries，a power audit text classification method with enhanced domain features was proposed. Firstly，a professional dictionary in power audit field was built and the EF-Doc2VecC model was proposed and combined with the professional dictionary text feature to obtain the enhanced feature text. The experimental results show that for the text classification of power audit with significant specialty，the EF-Doc2VecC model is 4，2，2，and 2 percentage points higher than the general domain text in terms of recall，sensitivity，precision，and F1 value classification index. In the general field，the EF-Doc2VecC model is 3，3，4 and 4 percentage points higher than the comparison method Doc2VecC in those evaluation indexes. In addition，comparing the classification performance of this method in vertical and general domains，the text classification performance in power audit field is 4，5，3 and 3 percentage points higher than that in general domain，respectively. Therefore，the text vector representation method and text classification method proposed in this paper can not only improve the text classification performance in the general field，but also significantly improve the finegrained classification performance in the vertical field.