Android has become the most popular mobile intelligent operating system with its open platform, diverse applications, and excellent user experience. However, at the same time, more and more attackers take Android as the primary target. The application store, which is the main download source for users, still does not have a complete security authentication mechanism. Given the above problems, we designed an Android application classification model based on multiple semantic features. Firstly, we use analysis tools to automatically extract the application’s dynamic and static features into the text document and use variance and chi-square tests to optimize the features. Combined with natural language processing (NLP), we transform the feature file into a two-dimensional matrix and use the convolution neural network (CNN) to learn features efficiently. Also, to make the model satisfy more application scenarios, we design a dynamic adjustment method according to user requirements, the number of features, and other indicators. The experimental results demonstrate that the detection accuracy of malware is 99.3921%. We also measure this model’s performance in detecting a malware family and benign application, with the classification accuracy of 99.5614% and 99.9046%, respectively.