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Conventional asynchronous motors are widely used in industrial production due to their simple structure, convenient maintenance, and high reliability. Therefore, it is of great significance to ensure the safe and stable operation of the frequency converter in the production environment. Motor fault diagnosis uses the characteristic current method, but in practical applications, the characteristic harmonics are separated, which makes it impossible to judge; the advanced long short-term memory (LSTM, long short-term memory) neural network and the newly proposed RAdam optimizer are used. When the motor is running normally, its operating characteristics are collected in real time. After the harmonics are extracted by the double-peak spectral interpolation method and the sliding window method, the output results of the motor are time series predicted and compared; finally, the actual motor data in the project is taken as an example. The feasibility of the algorithm is verified by measuring the actual data of its fault operation; it can be obtained through experimental tests, and it is used in traditional neural networks, and the algorithm has better fault detection capabilities;