To address the limitations of traditional generalized linear model (GLM) methods and explore the application value of pattern recognition in sports neuroscience. For binary classification between the shooting and skating groups (classification 1), the shooting and control groups (classification 2), and the speed skating and control groups (classification 3), the support vector machine (SVM) algorithm was employed using fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) as learning features. Additionally, the weight of each brain region in the classification algorithm was calculated. Leave-one-out cross-validation was utilized to calculate the classification accuracy. The pros and cons of the machine classification algorithm were evaluated based on overall accuracy, ROC curve, and prediction accuracy. Results show that higher accuracy and more stable classification performance are shown in Category 1 using the SVM algorithm, with a total accuracy (tACC) maintained above 96.67% and area under curve (AUC) of 1. This indicates that the functional characteristics of athletes' brain resting states across different tasks are excellently distinguished by the classification algorithm. In categories 2 and 3, the performance of the SVM algorithm is found to vary depending on the metrics used. Importantly, more stable classification performance is achieved by employing fALFF or the combined use of the three resting-state metrics, with tACC above 80% and AUC above 0.88 for both categories. A higher weight is assigned to the cerebellum by the classification 1 algorithm, indicating that the most significant difference in brain function activities among elite athletes of different sports lies primarily in the cerebellum. The application of SVM classification algorithm has achieved satisfactory results, demonstrating the practicality of pattern recognition methods in sports science. The research results offer a new perspective to enhance the understand of the relationship between exercise and the brain, which could prove valuable for researchers in the field of sports science.