The analysis and prediction of the shooting trajectory can be used to partially correct the shooting. The traditional automatic basketball shooting trajectory capture algorithm has a low capture accuracy and a long capture time, and thus is incapable of displaying the shooting trajectory in real time. To address this issue, this study proposes an automatic basketball shooting trajectory capture algorithm based on background elimination. The image of the basketball shooting trajectory is captured using imaging technology; the image is then preprocessed in four steps: binary erosion, binary expansion, closing operation, and opening operation to create a smooth image. After removing the background from the preprocessed image using the background difference method, the edge contour features are extracted, the candidate target area is set based on the extraction result, and a diagonal matrix reflecting the length and width of the trajectory target is introduced to calculate the probability of the color of the area in the shooting trajectory, thereby characterizing the trajectory. The target’s size changes in two directions to capture the basketball shooting trajectory automatically. The algorithm’s simulation results indicate that it has a higher accuracy and a shorter capture time.

With the increase of sports industry and various sports events, forecasting methods play an irreplaceable role in the competition system. At the same time of prediction, the selected calculation method, implementation scheme, model establishment, and other key implementation aspects have high technical requirements. In the whole prediction model, how to solve the problem of competition development is predicted and analyzed, and the best solution is selected for screening and evaluation, so as to significantly improve the prediction accuracy of the whole model. Understand the cause of the problem and solve it. Second, in the process of solving the problem, use the relevant forecasting technology theory to determine the weighted weight coefficient method of the combination forecasting model. In this paper, before the competition, select the best combination of forecasting model to sports-related personnel simulation cases and form a comparative analysis. Finally, through the combination of prediction experimental methods for the effective results of the problem, and in the later development process to get a new prediction model. In the actual process of forecasting, facing the complex combination of forecasting systems, the selected evaluation theme and the uncertainty of objects will produce great forecasting errors. Through excellent improvement, the defects of the combined forecasting model have been overcome, and the forecasting accuracy has been improved, which will greatly enhance the good development of physical education. The coordination mechanism, guarantee mechanism, and competition organization mechanism of sports competition alliance should be analyzed through prediction model. Spread Chinese sports culture, improve the level of sports competition, and carry out research and analysis on the prediction model of sports competition. The experimental results in this paper show that (1) the prediction process is generally tested in extremely unstable environment, so it will have a certain impact on the prediction accuracy, that is, there are data with the highest measurement accuracy of 0.99 and the lowest measurement accuracy of 0.92. (2) Different calculation methods will be selected for different prediction models of competitions. For example, the error coefficients of SSE are 1.6859, 1.8338, and 1.6161, respectively, which proves that different models have different contents in prediction. (3) The comprehensive promotion of sports competition will need more prediction models to select and promote. In the progress of the times, the prediction value shows an increasing trend, from 2.4 billion cubic meters to 3.3 billion cubic meters, which is the perfect realization of the prediction model. (4) In the structure of the forecasting model, the weighted geometric combination forecasting model is obtained by the statistical investigation and analysis of relevant personnel, which is the best combination forecasting model of sports competition with the optimal weight coefficient of 1.9, the forecasting value of 33.95, and the forecasting accuracy of 0.9996.