Research on the Design of Financial Data Analysis Platform with Joint PSO-HQCNN and RPA Robot Visualization Technology

A novel RPA technique for financial data analysis based on quantum neural network model is innovatively developed. Compared to the conventional model, the Hybrid Quantum Convolutional Neural Network model (HQCNN) reduces the loss value by 0.0056% and improves the training and testing accuracy by 0.93% and 0.36%, respectively. In addition, the hybrid quantum convolutional neural network model of this RPA exhibits less fluctuation in loss value after 60 iterations and remains stable after 90 iterations. Meanwhile, we combine the particle swarm optimization algorithm with HQCNN to transform classical data into quantum state data for joint optimization. The results of the recognition and validation accuracy plot show that PSOHQCNN has the best recognition effect, with a recognition accuracy of 96.85%, followed by HQCNN with a recognition accuracy of 96.20%. And CNN has the lowest recognition accuracy of 95.65%. However, PSO-HQCNN model increases the time cost while improving the accuracy. Therefore, in the process of applying RPA to financial systems, trade-offs should be made according to the actual situation, and the HQCNN model or PSO-HQCNN model should be selected. In conclusion, through the implementation process of RPA robot visualization application established by using the quantum neural network model, this study provides a new direction of exploration and a feasible implementation path for the automated process processing of enterprise financial data analysis.