This paper establishes shipping enterprises' safety culture system model based on the dynamic calculation matrix model. This paper calculates the deep learning theory's shipping safety early warning system. At the same time, we use the fuzzy evaluation method to establish the relevant weights of the safety culture index system. The method model mentioned in this paper effectively avoids the subjectivity of the pure expert evaluation method. We use experimental simulations to verify the effectiveness of our model. Experiments show that the system in this paper has the advantages of reliable prediction results and high precision.

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In view of the problem of course tracking control of under-driven USV under complex external environment, the adaptive control law is designed by constructing an iterative sliding mode function and using Lyapunov stability theory on the basis of the kinetic model of ship motion. The RBF neural network control technology and adaptive control technology are integrated into the control algorithm, and the iterative sliding mode heading tracking controller of the unmanned surface ship adaptive-neural network is designed. The online reinforcement learning of the RBF neural network is carried out by the reinforcement learning algorithm, which enhances the approximation performance of the network. Besides, the particle swarm optimization with shrinkage factor is applied to the optimization for control parameters to enhance the adaptability and robustness of the control system. The performance experiment verifies the effectiveness and practicability of the adaptive iterative sliding mode control algorithm for unmanned surface ships based on reinforcement learning and particle swarm optimization algorithm; meanwhile, comparative tracking experiments verify that the comprehensive performance of the proposed USV’s course tracking control system is better than that of the genetically optimized neural network sliding mode control system. Therefore, the fusion of multiple algorithms can be applied to improve the performance of the control system.