The invention relates to an instruction-set-irrelevant binary code similarity detection method based on a neural network. The method mainly includes the steps that binary files are reversely analyzed, and 24 features on the 9 aspects of call relation features, character string features, stack space features, code scale features, path sequence features, path basic features, degree sequence features, degree basic features and map scale features of functions are extracted. Based on expression forms of the features, the similarity degrees of the 24 features of the two to-be-compared functions are calculated through 3 similarity calculation methods and serve as input vectors of an integrated neural network classifier, and predicted values of the overall similarity between the two functions are acquired and ranked. Compared with the prior art, dependence on specific instruction sets is avoided, similarity detection of binary files of different instruction sets can be achieved, accuracy is high, the technology is simple, and popularization is easy.