With the wide application of power monitoring system

the management of cyberspace assets is becoming more and more complex

facing the problems of various data and unknown classification.To solve the above problems

the research on the construction and prediction recognition method of the cyberspace asset portrait of the power monitoring system was proposed.It proposes a prediction recognition method combined with MLP_LSTM model

which constructs a preliminary portrait of assets through asset information collection and feature extraction.Then

MLP is used to perform the nonlinear mapping of features

and LSTM model is introduced to capture the time series information of assets to achieve more accurate prediction and recognition.The simulation experiments show that

compared with the traditional method

the proposed method not only significantly improves the management efficiency and security

but also further improves the accuracy of prediction and identification through the introduction of MLP_LSTM model

which provides strong technical support for the intelligent management of cyberspace assets of the power monitoring system.