Based on wireless infrared temperature sensor and data acquisition terminal, a distributed temperature monitoring system for offshore electrical equipment was constructed to meet the actual requirements of temperature monitoring for offshore electrical equipment. The system application software was developed to realize continuous temperature monitoring for offshore electrical equipment. Aiming at the problem that traditional temperature prediction is difficult to deal with a large amount of fluctuating data and has limited ability to deal with time series, a combined prediction method of Bayesian optimization and LSTM was proposed. The temperature characteristics of the transformer during operation were analyzed by taking the transformer monitored offshore platform as the research object. The LSTM network prediction model with strong timing was adopted and the Bayesian optimization algorithm was introduced to train and update THE LSTM parameters. The practice shows that the LSTM model based on Bayesian optimization has good prediction effect on transformer temperature of offshore platform, and its root mean square error is 0.139 and prediction accuracy is 98.56%. Through the comparative analysis of four prediction models including support vector machine, BP neural network, LSTM and Bayesian LSTM, the advantages of Bayesian optimized LSTM model for offshore transformer temperature prediction are confirmed.