The learning vector quantitative(LVQ) neural network and deep learning algorithm cyclic recurrent longterm and short-term memory(LSTM) neural network in state analysis of power station equipment are analyzed in detail. The LSTM neural network is applied to carry out state analysis for the coal mill equipment

the excitation function of the last hidden layer in the LSTM neural network is set as Softmax function

and the output value of the LSTM neural network indicates the health degree of the device state and the probability that the device may have an accident. Moreover

the equipment state analysis result obtained by the LSTM neural network is compared with that of the LVQ neural network. The results show that

the training model obtained by the LSTM neutral network can obtain a higher classification accuracy and reduce the rate of missed and false alarm in equipment condition evaluation.