In the application of engineering materials，the elastic modulus is an important performance parameter. Finding the materials with specific elastic properties is a hot issue in the field of new materials synthesis. How to predict the elasticity quickly and accurately is of great significance in engineering. It is not practical to measure the elastic properties of a large number of materials by practical experiments. Therefore，screening material data through computer simulation，selecting candidate materials,and then verifying them through actual experiments is an ideal method for new material discovery. At present，the main calculation methods for material performance prediction are first-principles high-throughput calculation，which is inefficient and difficult to ?
complete the task of high-volume material screening efficiently. The machine learning prediction method based on material statistics can quickly predict material properties through big data mining，which has become a possible alternative to high-throughput computing. In this paper，feature selection method and machine learning model are combined to select the most effective combination scheme of elastic modulus prediction，and interactive interface is designed to conduct visual analysis of the relationship between input features and elastic properties of materials. The experiment shows that the Pearson/RFE-GBDT combination model has the best performance. Meanwhile，through the visualization analysis，it is found that the energy of each atom，melting point，density and other characteristics have a great influence on the prediction results. These important characteristics can be used to preliminarily predict the range of elastic modulus from the feature-target relationship，and the value of target attributes can be used to estimate the important characteristics of the material in turn. These results can be applied to explore the influencing factors of elasticity，predict the properties of large quantities of materials and guide the synthesis of materials through visualization analysis.