Illegal insider trading identification is of great significance to the healthy development of the securities market. However, with the development of information technology, problems such as multidata sources and noise bring challenges to the insider trading identification work. Moreover, most of the current research on insider trading identification is based on single-task learning, which treats enterprises in different industries as a whole. This may ignore the differences between insider trading identification in different industries. In this article, we collect indicators from multiple sources to help regulators identify insider trading and then use information gain and correlation analysis to screen the indicators. Finally, we propose a multitask deep neural network with insider trading identification in different industries as different subtasks. The proposed model takes into account the correlations and differences between different tasks. Results of experiments show that compared with logistic, support vector machine, deep neural network, random forest, and extreme gradient boosting model, the proposed model can identify insider trading of enterprises in different industries more accurately and efficiently. This article provides new ideas for market regulators to maintain the order of the securities market through intelligent means.