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The traditional machine learning-based malware classification methods are mainly based on feature engineering. In order to improve accuracy, many features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve this issue, this paper proposes a malware classification method based on the word vector of bytes in the malware sample and Multilayer Perception (MLP). A malware sample consists of large number of bytes with values ranging from 0x00 to 0xFF. Therefore, every malware sample could be considered as a document written by bytes. And this document could be divided into sentences based on padding or meaningless bytes. In this paper, first, we use Word2Vec to calculate a 256 dimensions word vector for each byte. Second, we combine them into a matrix in ascending order. Third, we use MLP to train the model on the training samples. Finally …