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Objective To investigate the influencing factors for sleep disorder during parachute training in special operations forces from a unit of the army, and to develop and validate a prediction model. Methods A total of 349 special operations officers and soldiers undergoing parachute training from October to November 2022 were recruited as research objects. Self-made questionnaires, Self-Rating Anxiety Scale (SAS) and Pittsburgh Sleep Quality Index (PSQI) Scale were used to collect their baseline data and assess sleep quality. According to their PSQI score, the participants were divided into sleep disorder group and non-sleep disorder group, and then assigned into a training set (n=228) and a validation set (n=97) in a ratio of 7∶3. Based on the training set, univariate analysis and multivariate logistic regression analysis were used to screen out the factors influencing the risk of sleep disorders. Then R language was employed to draw a nomogram model, and receiver operating characteristic (ROC) curve was plotted to evaluate the discrimination of the model. Calibration curve analysis and Hosmer-Lemeshow goodness-of-fit test were applied to evaluate the accuracy of the model, and decision curve analysis (DCA) was conducted to evaluate the validity of the model. The validation set was used for internal validation. Results There were totally 325 participants with valid data, and all of them were male. They had a total PSQI score of 4.26±3.30, and 62 of them (19.1%) had sleep disorders, including 46 (14.1%) of mild, 14 (4.3%) of moderate, and 2 (0.6%) of severe sleep disorders. Based on the 228 participants in the training set, the results of univariate and multivariate logistic regression analyses showed the number of parachute jumping (OR=0.390, 95%CI: 0.185~0.811), smoking (OR=2.980, 95%CI: 1.352~7.028), anxiety state (OR=3.280, 95%CI: 1.434~7.570), and chronic pain (OR=4.090, 95%CI: 1.952~8.690) were independent influencing factors of sleep disorder in these parachute officers and soldiers. A risk prediction model for sleep disorder was constructed, and the area under the curve (AUC) value was 0.778, with an optimal threshold of 0.163, a specificity of 63.6% and a sensitivity of 84.1%, with good discrimination. The quasi-curve and Hosmer-Lemeshow goodness-of-fit test (Chi-sqaure=8.789, P=0.456) showed that the model had good accuracy, and the clinical decision curve indicated that the prediction model had good clinical applicability. The model was confirmed to be valid with internal verification based on the validation set of 97 participants. Conclusion Special operations forces with parachute training have different degrees of sleep disorders. Number of parachute jumping, smoking, anxiety and chronic pain will aggravate sleep disturbances.