Non-supervision feature extraction method based on self-coding neural network

The invention provides a non-supervision feature extraction method based on a self-coding neural network. According to the method, firstly, training data matrix building is performed; then, each component value of a training data matrix is normalized to a position between [0,1]; next, parameter study is performed to obtain a self-coding neural network model; then, the output of a hidden layer is calculated; features are obtained; finally, the number of optimum hidden layer nerve cells is determined according to a halving value taking method; finally, a structure of the self-coding neural network is determined. In the network training study, the expected output of the self-coding neural network is specified to be equal to the input of the network; through such a study target, the providing of the expected network output by training data is not needed in the training process of the self-coding network training process. The method provided by the invention has the advantages that under the condition of being lack of priori knowledge, an internal rule of the equipment mass state data can be excavated and features can be extracted.