With the continuous improvement of the electrification level of smart fishing grounds, the proportion of environmentally sensi?tive loads in fishing grounds increases. Load forecasting should fully consider the impact of environmental climate change on the operationof fishery electrical equipment. Firstly, the data collected by the fishing ground monitoring platform are analyzed and preprocessed, andthe typical scenes are divided according to the climatic characteristics. Secondly, the least square method is used to decompose the meteo?rological load affected by the climate, and the correlation coefficient method is used to analyze the correlation between the load and the me?teorological index. The principal component analysis method is used to transform multiple single meteorological indicators into a few com?prehensive indicators in SPSS. The comprehensive meteorological indicators and meteorological load data are used to train the BP neuralnetwork, and the load forecasting model of fishery electrical equipment based on BP neural network in typical scenarios is constructed. Fi?nally, an example is verified based on the operation of fishery electrical equipment and climate index data on the target day. The resultsshow that the model reflects the impact of environmental climate change on the load of fishery electrical equipment, and the error accuracymeets the actual engineering needs.