The invention relates to an LSTM-based equipment evaluation method and system, and relates to the field of intelligent transportation. The method comprises the following steps: constructing an input feature vector of an LSTM model, wherein the input feature vector comprises state data of the equipment; constructing a training set, a verification set and a test set based on historical data; constructing an initial LSTM (Long Short Term Memory) model; training, verifying and testing the initial LSTM model, and generating a final LSTM model; and based on equipment state data acquired in real time, evaluating the equipment state and the abnormality type through the LSTM model. According to the equipment evaluation method and system based on the LSTM, the running state of the future equipment is predicted based on the historical state sequence data of the equipment, such as the current, the voltage, the chip temperature, the idle memory size, the fault time, the fault type and the network packet loss rate, so that the real-time performance and the accuracy of equipment state monitoring are improved.