Abstract: With MySQL Workbench 8.0 as the database platform and adopting a three-layer browser/server architecture, a C/SiC composite database system based on Web interface and machine learning technology was developed by PyCharm 2022. The system included material database and machine learning model, and had the functions of information query, addition, modification, deletion and search, as well as support information management, design analysis view, case-based reasoning aided design, user and system management for material component structure, preparation process, test performance and other stages of material research and development. The machine learning model was trained based on the neural network regression algorithm, the mechanical properties of materials were predicted by the microstructure parameters of materials, and the influence weights of different microstructure parameters were evaluated and deployed in the system platform. The machine learning model was invoked through the Web user interface to predict the tensile modulus and the flexural modulus of the reserved validation set data. The results show that the accuracy of machine learning models in predicting material mechanical properties reached about 95%, and the trained predictive model exhibited good accuracy and generalization capability.