At the moment, the detection technology of malicious code based on deep learning is a research hotspot in the field of malicious code detection. However, most researches focus on how to improve the algorithm to enhance the detection accuracy of malicious code, but ignore the lack of sample tags in the data set of malicious code, failling to train high-quality models. In this study, the problem of detecting isolated islands of data samples and data trustworthiness of malicious code is solved by Blockchain technology, and code features are extracted with the Markov graph algorithm. The training fusion block chain based on distributed deep learning has the advantages of decentralization, traceability and non-tampering, and the contributors of different computing power adopt synchronous training to update model parameters. The feasibility and great potential of this method are verified by simulation experiments and theoretical analysis.