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To address the problem of intelligent hit prediction of free throws in the context of smart sports, the study proposes a target detection network based on the improved loss function of YOLOv5 network and the introduction of an attention mechanism to complete the accurate identification and effective extraction of free throw players in game videos. Then a recognition method based on trajectory optimization is designed for the pose detection of free throw players, and a support vector machine (SVM) classifier is used for basketball free throw hit prediction. The experimental results show that the target detection algorithm proposed in the study achieved an average accuracy of 85% after 15 rounds of training, while the accuracy of the hit prediction algorithm also increased by 5%, which is superior to other algorithms in the experiment. The experimental results show that the improved Loss function and the introduction of attention mechanism can effectively detect the area where the free throws are located, and accurately extract the close-up video of the free throws. Basketball coaches can combine posture estimation and free throw hit prediction data based on experience to effectively evaluate movement performance and provide guidance suggestions. This can not only achieve posture correction but also help evaluate athlete level, thereby establishing intelligent athlete data archives.