Utilizing 3D Point Clouds and Deep Learning Technology in Multimedia Instruction for Sports Mechanics

In this paper, an optimization model based on 3D point cloud and deep learning theory is constructed, which improves the fluency and accuracy of multimedia sports operation. The model has performed well in sports mechanics teaching and proved its outstanding performance. It is found that the changes of the three kinds of point clouds have different laws, and the spherical neighborhood gradually decreases, while the adjacent neighborhood shows an upward fluctuation trend. The neighborhood of cylinder shows linear and nonlinear characteristics. The parameter curve of point cloud model fluctuates greatly, and the curve corresponding to function output value fluctuates widely, while the curve corresponding to feature extraction method fluctuates relatively little. The ability training index plays a significant role in improving the model output, the multimedia input index fluctuates well, and the teaching method index shows an approximate linear change law. The accuracy of the model is verified by data calculation method, which provides theoretical guidance for multimedia sports mechanics teaching.