Objective To explore the value of dynamic prediction model based on recurrent neural network (RNN) algorithms for dynamic prediction of clinical outcomes in patients with critical trauma, and to study the feasible construction scheme and path of dynamic strategy and real-time prediction model. Methods The data of this study were derived from the US Medical Information Mart for Intensive Care (MIMIC) -Ⅳ 2.0. In order to predict the in-hospital outcomes of critical trauma patients, 2 RNN algorithms, long short-term memory (LSTM) and gated recurrent unit (GRU) were used to train dynamic prediction models under the time windows of 4, 6 and 8 h, respectively. The performance of the models was evaluated using the sensitivity, specificity, F1 value and area under curve (AUC) value; and the effects of different RNN algorithms and time windows on the performance of the models were analyzed. Hidden Markov model (HMM), random forest (RF) model and logistic model were trained under 8-h time window as the controls to compare the performances and the time trends horizontally with the 2 RNN algorithm models. Results There were significant differences in the 4 performance indexes of the RNN dynamic models including the sensitivity, specificity, F1 value and AUC value (all P＜0.001), and the performance indexes at 8-h time window were higher than those at 6 h and 4 h; there was only significant difference in specificity between different RNN algorithms (LSTM & GRU) (P＝0.036). The results of the horizontal comparison showed that there were significant differences in each performance index between the 2 RNN prediction models and other models (all P＜0.001), and each index of the 2 RNN algorithm models was higher than those of the HMM, RF model and logistic model. The intraclass correlation coefficients (ICCs) of each algorithmic model were less than 0.400 for the sensitivity, specificity and F1 value (0 was not included in 95% confidence interval ［CI］), while the ICCs for the AUC value were statistically under-evidenced (0 was included in 95% CI). Conclusion The dynamic models based on RNN algorithms have certain performance advantages over those based on other common algorithms, and the time window may have an impact on the model performance.