Design and Implementation of Application Classification Based on Deep Learning

This paper uses a deep learning-based model to solve the problem of automatic classification of mobile applications. In this paper, we address the classification problem of mobile applications from the perspective of text classification. By analyzing the major mobile phone application markets, we have developed the main categories of applications, and crawled the descriptions of various mobile phone applications as needed. With analyzing the original corpus of the crawl, the semantic information is further expanded by using data augmentation methods based on both word and char. Then, we design different text classification networks and compare the experimental results, and finally select the network with the best classification effect for tuning. The results of experiments show that the classification network of Bert+Highway+GRU designed in this paper has better classification effect. The average P/R/Fl value of the classification is 0.8820/0.8892/0.8856. The classification indicators under the above all reached 0.85 or higher, which in the first level label of those applications; at the same time, it also showed better performance in network training and convergence speed. The deep learning-based mobile phone application classification network designed in this paper has high classification efficiency and can achieve higher classification accuracy.