Evaluation of corporate financial performance based on bionic algorithm and biomechanical analysis

The evaluation of corporate financial performance plays a critical role in driving enterprise transformation and fostering industrial development. To enhance the accuracy of financial performance evaluation, this study integrates knowledge from biomechanics and bioinformatics, exploring the application of a bio-inspired immune algorithm-optimized convolutional neural network (CNN) in financial performance evaluation. A biomechanics-based model is constructed using CNN to simulate the “mechanical response” of financial performance evaluation. By simulating the structure of biological visual systems, CNNs can effectively extract local features from input data, enabling efficient classification and recognition. During the optimization process, the biological immune algorithm adjusted hyperparameters such as the learning rate and kernel size through mechanisms of selection, reproduction, and mutation. The application of biologically inspired algorithms in deep learning effectively enhanced the model’s adaptability and robustness, providing new ideas and methods for financial performance evaluation and validating the effectiveness of bionic algorithms in complex tasks. In the experiments, a GRA-Entropy-SOM-CNN model was constructed, with initial test results showing an accuracy of 97.18% in the task. However, by introducing the biological immune algorithm to optimize the CNN, the final model achieved an accuracy of 98.5% on the test set, demonstrating significant performance improvement.