The cost of misclassifying a malware program as normal is often higher than that of misclas-sifying a normal program as malware. Therefore, how to improve the detection accuracy of malware programs is a very important problem. This paper proposes a deep learning malware program detection algorithm based on attention mechanism. Word2Vec model is used to map the Application programming interface (API) into word vectors, and all word vectors of each sample are arranged into a matrix with the same size. On this basis, residual network is used to extract features of samples. The features are input into the attention mechanism to learn the similarity between samples. Then, the features are weighted with the similarity to obtain the new features with better robustness. The new features and the original features are added element by element to obtain the sample features more suitable for classification. Finally, samples are classified by classifier. Experiments show that the classification effect of the proposed method is better than that of the traditional machine learning method.