The invention relates to a method for distinguishing gene mutation types an individual tumor sample based on second-generation sequencing. A tumor tissue sample and a normal tissue sample are used forlibrary construction and NGS sequencing respectively, strand bias and different types of base frequencies of mutation sites stored in an intermediate file BAM for biological information analysis of the tumor tissue sample are analyzed, the quality of base comparison and the frequency of noise are used as the training characteristics of machine learning, meanwhile, type information of corresponding mutation sites of the normal tissue sample is paired to serve as a prediction mutation type, a classification prediction model is constructed to distinguish somatic mutation from germline mutation,the model is used to distinguish somatic mutation from germline mutation, the detection efficiency is high, the specificity is high, and after the model is established, the individual tumor sample canbe used for NGS sequencing and mutation detection, the detection cost of a normal or cancer sample can be well saved, and meanwhile, the problem that normal tissues of tumor patients with specific types are difficult to obtain can be solved.