Concurrent neural network-based file fragment classification method and device

The invention discloses a concurrent neural network-based file fragment classification method and device. The method comprises the following steps of: firstly extracting file contents in file fragments; converting the extracted file contents into hexadecimal first character string sequences; taking the character string sequences as input information of a concurrent neural network deep learning algorithm; and finally inputting the first character string sequences into a pre-constructed trained concurrent neural network algorithm-based classification model, and judging types of the file fragments by utilizing the classification model. According to the method, through a learning process of carrying out automatic feature extraction on feature vectors of fragments on the basis of a concurrent neural network classification algorithm, the types of file fragments can be classified. 本发明公开种基于循环神经网络的文件碎片分类方法及装置，所述分类方法首先需要提取文件碎片中的文件内容；然后将提取的所述文件内容转换为十六进制的第字符串序列；将这些字符串序列作为循环神经网络深度学习算法的输入信息；最后将所述第字符串序列输入到预先构建的已训练的基于循环神经网络算