Abstract: Composite material is a new type of material based on different combinations of various material components. It has been widely used in transportation, construction and other fields because of its excellent comprehensive properties during recent years. Despite of the large errors between experimental analysis and empirical analysis result sometimes,it is of great theoretical significance to establish a credible theoretical analysis to verify the bearing properties of composite materials.Considering that the forecast accuracy is affected by the length of the data sequence when using the memory characteristics of LSTM to predict the load, a load forecast method of composite materials combined with LSTM network and Kalman filtering is proposed. The model can learn from the data avoiding the dependence of traditional Kalman filtering on the dynamic model, at the same time the influence of training data sequence length on traditional LSTM can be overcome to some extent.The results show that the method proposed in this paper can obtain more excellent predictive performance:(1)The performance of LSTM-KF is better than the independent LSTM, and the prediction curve of LSTM-KF is closer to the actual load value;(2)The prediction error of LSTM-KF reduces that of LSTM from 0.033 0 kN to 0.016 0 kN, a decrease of 51.52%.

Key words: composite materials, load forecast, long-short term memory network, Kalman filter