Abstract: Abstract: Background　Fatigue is one of the crucial factors threatening people's health, and accurate assessment of fatigue levels and intervention are of great significance. Objective　To construct and analyze the evaluation model for exercise-induced fatigue in healthy young male subjects.Methods　In April 2023, 192 healthy young male subjects were recruited as the modeling training set by the Anesthesiology Department of the First Medical Center of Chinese PLA General Hospital. Another 33 young male subjects from different units were recruited as the validation set. All subjects were evaluated using the "Self-reported Symptoms of Fatigue Questionnaire". Non-invasive blood pressure was collected through the blood pressure postural reflex test, and heart sound and electrocardiogram data were collected using wearable heart sound and electrocardiogram devices. The data were processed according to (the value 2 minutes after being lifted up and recovered from lying flat - the value before lying flat) / the value before lying flat. Univariate and multivariate logistic regression analyses were performed to analyze the related factors for the occurrence of exercise-induced fatigue in this population, and the corresponding nomogram evaluation model was established. The receiveroperating characteristic (ROC) curve was used to analyze the efficacy of the model. The calibration curve was drawn to evaluate the calibration degree of the model, and the decision curve analysis (DCA) was drawn to evaluate the clinical applicability of the model. Results　In the training set, multivariate logistic regression showed that △PEP (OR=1.067, 95% CI: 1.011-1.139), △S2E (OR= 1.019, 95% CI: 1.003-1.0390), and △HR (OR=1.115, 95% CI:1.033-1.215) were risk factors for moderate to severe fatigue; △S1E (OR = 0.972, 95% CI: 0.943-0.997) and △LVETc (OR= 0.772, 95% CI: 0.613-0.932) were independently associated with moderate to severe fatigue. Incorporating the above factors and establishing a nomogram evaluation model, the area under the curve (AUC) of the training set of this model was 0.908 (95% CI: 0.825-0.991). The AUC of the validation set was 0.940 (95% CI: 0.854-1). The calibration curve showed that there was a high degree of consistency between the verification results and the model results; The decision curve showed that the model performed well within the low to medium risk threshold range. Conclusion　The logistic regression model in this study utilizing heart sound and electrocardiography data demonstrates good performance in assessing postexercise fatigue levels and has certain application value.