Design and Simulation of Economic Law Risk Control Technology Path Based on Deep Learning Algorithm

This paper proposes a dynamic economic law risk control technology path based on deep learning. By integrating multimodal legal text analysis and real-time behavior monitoring data, this paper constructs 15 mathematical models for the dynamic quantification of risk value to interpret economic law provisions (with an accuracy rate of 92.7%), and establishes a knowledge graph of legal elements. Secondly, a spatio-temporal attention mechanism (STA-GRU) is designed to process the time series data of enterprise operations, enabling the tracking of risk transmission paths. Finally, a policy response simulation system is developed to quantify the compliance cost elasticity under different regulatory intensities (η = 0.38 ± 0.05). Empirical results show that compared with traditional methods, this mathematical model improves the F1 value for contract fraud by 27.3%, When the regulatory threshold is set at P ≥ 0.82, the enterprise violation rate decreases by 41.2% while the compliance cost only increases by 18.7%. This research provides an interpretable and iterative technical path for intelligent supervision of economic law-related risks.