Abstract: As one of the important embodiments of intelligent finance, intelligent trading based on trading software is booming in the domestic financial market, significantly improving the efficiency of financial transactions. However, there are various types of intelligent trading software, and the design ideas and algorithm complexity of the trading strategies involved are diverse, leading to abnormal trading and non-compliance risks. Currently, the work of anomaly detection for intelligent trading behavior has not been fully developed. Therefore, aiming at the complexity and professionalism of the trading scenario, a method combining deep learning implicit representation learning and rule tree model explicit rule learning is proposed to model the timeliness and compliance of the trading data respectively. To verify the effectiveness of the proposed method, it has been compared with some representative benchmark methods in multiple scenarios such as stocks and futures, and the best performance has been achieved. In addition, further analysis of the model has been conducted to verify the impact of different features on the effectiveness of abnormal detection.