机器学习技术是当前的研究热点，以其强学习力、高通用性广泛应用于各类预测、识别、分类任务中。探讨了机器学习在计算结构力学中的应用，重点分析了其在材料性能预测、结构损伤分析、传统方法改进、本构方程建立和微分方程求解中的作用。通过文献综述总结了机器学习算法如神经网络、支持向量机和随机森林在提高计算效率和设计流程优化方面的优势。研究指出，机器学习与经典计算方法的结合为工程问题求解提供了新途径。未来研究将聚焦于算法优化、模型改进和跨学科技术融合。

Machine learning technology is a hot research topic at present. It is widely used in various prediction, recognition and classification tasks with its strong learning ability and high versatility. The application of machine learning in computational structural mechanics was discussed, with emphasis on its role in material property prediction, structural damage analysis, improvement of traditional methods, constitutive equation establishment and differential equation solving. Through literature review, the advantages of machine learning algorithms such as neural networks, support vector machines and random forests in improving computational efficiency and design process optimization were summarized. It is pointed out that the combination of machine learning and classical computing methods provides a new way to solve engineering problems. Future research will focus on algorithm optimization, model improvement and interdisciplinary technology integration.