The speed of evolution in mobile application (APP) software market is accelerating. Effective analysis of software defects can help developers understand and repair software defects in time. However, the analysis object of existing research is not enough, which leads to isolated, fragmented information, and poor information quality. In addition, because of insufficient consideration of data verification and version mismatch issues, there are some errors in the analysis results, resulting in invalid software evolution. In order to provide more effective defect analysis results, an APP software defect tracking and analysis method oriented to version evolution (ASD-TAOVE) is proposed. First, the content of APP software defects is extracted from multi-source, heterogeneous APP software data, and the causal relationship of defect events is discovered. Then, a verification method for APP software defect content is designed, which is based on information entropy combined with text features and structural features to calculate the defect suspicious formula for verification and construction of APP software defect content heterogeneity graph. In order to consider the impact of version evolution, an APP software defect tracking analysis method is designed to analyze the evolution relationship of defects in version evolution. The evolution relationship can be transformed into the defect/evolutionary meta-paths which are useful for defect analysis. Finally, this paper designs a heterogeneous information network based on deep learning to complete APP software defect analysis. The experimental results of four research questions (RQ) confirmed the effectiveness of ASD-TAOVE method of defect content verification and tracking analysis in the process of version-oriented evolution, and the accuracy of defect identification increased by about 9.9% and 5% respectively (average 7.5%). Compared with baseline methods, the ASD-TAOVE method can analyze more APP software data and provide effective defect information.