Innovative application of deep learning and genetic algorithm based on biomechanics in enterprise economics and audit management

With the increasing demand for enterprise economic management in complex and dynamic environments, the interdisciplinary application of biomechanics has shown significant potential. This article explores the innovative practices of genetic algorithms and deep learning in optimizing enterprise economic management. Genetic algorithm simulates biological evolution mechanisms such as natural selection and genetic variation to achieve multi-dimensional and multi-level optimization of enterprise economic models, improving decision-making efficiency and adaptability. Deep learning draws on the structural characteristics of biological neural networks to solve problems such as insufficient data and model overfitting, optimizing the intelligence level of resource allocation, performance evaluation, and strategic planning in enterprise management. On this basis, this paper introduces the concept of biomechanics to further improve the adaptability and efficiency of the model. Biomechanics emphasizes the movement and adaptability of organisms in complex environments, which provides a new perspective for corporate economic management. By simulating the dynamic adjustment mechanism of organisms in the face of external pressure, enterprises can respond to market changes more flexibly and optimize resource allocation and decision-making processes. In addition, this article proposes a comprehensive framework that combines multi-level genetic algorithms and deep learning, and verifies its effectiveness in dynamic market environments through case studies. Research has shown that biomechanics not only provides theoretical support for enterprise economic management, but also offers efficient and sustainable pathways for solving complex economic problems. By incorporating the inspiration of biomechanics into the optimization practice of corporate economic management, enterprises can better adapt to market changes, improve the flexibility and efficiency of decision-making, and point the way for future economic management innovation.