Focusing on predicting the parachute deployment load in the process of inflation accurately, a compensation calculation method of parachute deployment load with RNN (recurrent neural networks) was proposed, including the model architecture and data processing. The predicted value calculated by inflation time method was brought into the RNN for the secondary calculation, so that the final result could be close to the airdrop experiment data. The feedforward neural networks, standard recurrent networks and long short-term memory networks were used to compare the model characteristic. The research verified the applicability and accuracy of the prediction results and analyzed the effects of hyperparameters such as learning rate, input layer dimension and hidden layer dimension on the performance. The optimal training condition for reference to the compensation model was developed through the test. The results show that the utilization of RNN for parachute deployment load prediction is effective and provides a referential significance for the interdisciplinary research of machine learning and parachute industry.