To adapt to the complex and volatile economic environment, the significance of a company’s net profit growth forecast for investors’ decision-making, corporate strategic planning, and market regulation has become increasingly prominent. Although the existing prediction methods have made certain progress, their prediction performance is still restricted due to the difficulty in fully capturing the temporal correlation of complex financial data and the low efficiency of model parameter optimization. In view of this, this paper proposes the PSO-Transformer classification algorithm. By integrating the advantages of the Particle Swarm Optimization (PSO) algorithm and the Transformer model to break through the bottleneck, data statistical analysis and correlation analysis are carried out first. And Decision tree, Random Forest, AdaBoost, GBDT, ExtaTrees, CatBoost and XGBoost were selected as the comparison models. From the perspective of evaluation indicators, the Our model has significant advantages in the core dimensions of error control and goodness of fit: its MSE is 29.098 and RMSE is 5.394, both being the lowest among all models. It can optimally control the square term and root mean square error of the prediction error, effectively reducing the interference of large errors on the overall prediction results. The R2 reached 0.432, the highest among all models, significantly higher than AdaBoost (0.369), which ranked second. It has the best ability to explain data variation and the best fit, and can more accurately capture the inherent patterns of the data. The MAE is 4.482, which is slightly higher than ExtaTrees (4.355) and AdaBoost (4.379), but lower than most models such as Random Forest (4.529), CatBoost (4.418), and XGBoost (4.805). The average absolute error control is at an excellent level. Overall, this model performs outstandingly in reducing squared errors and improving the goodness of fit. Only the control of relative errors needs further optimization. It provides an effective new path for improving the accuracy of the company’s net profit growth prediction and has significant practical significance for optimizing investment decisions, assisting enterprise strategic planning, and enhancing the scientific nature of market supervision.
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