With the rapid development and popularization of intelligent terminals, app software has also developed rapidly. The research and practical value of mining user experience (UX) of app software form interaction information are becoming increasingly prominent. The interactive information of app software is multisource homogeneous and heterogeneous. In order to obtain more accurate and more comprehensive app software UX results, the fused multisource information should be analyzed. In this paper, the app software UX analysis method based on multisource information fusion is proposed. First, feature engineering is carried out to extract the features. Then, the feature combination tree is constructed after feature correlation mining. Finally, the multisource app software interactive data are fused, and the result is further analyzed to obtain the information of app software UX. The experiments clearly show that the method can effectively fuse multisource app software interaction data and help to comprehensively mine the app software UX embodied in the data.

While mobile application (app) software is becoming increasingly important in people's daily lives, researchers have the limitation of understanding the details of user operations inside the app. With the update of the Android system and application user interface, relying on manually defined user operation event templates or modifying the app source code can no longer meet the needs of fine-grained user operation analysis in multiparallel applications. In this article, a novel method is proposed for effectively analyzing user operations in parallel apps based on the temporal context of user operation sequences. The authors provide a general framework in the Android system to parse out fine-grained user operations. In addition, the authors build a deep learning model with LSTM-TextCNN to complete user operations in parallel app from global temporal context and app temporal context. The authors collected 240k operations of 12 users over a month. Comparative experiments with a baseline show that the proposed method can efficiently and accurately analyze parallel app user operations.