The short-term load forecasting model using long short-term memory(LSTM) network has the problem of feature redundancy and loss of important information. In order to solve these problems, a shortterm load forecasting method based on feature selection strategy and DLSTMsFCN is proposed. Firstly, the feature optimization strategy based on extreme gradient boosting(Xgboost) is adopted to improve the feature redundancy problem of load prediction input. Secondly, DLSTMs are used to extract the time series features of load data, and the highresolution information is extracted through the multidimensional convolution operation of FCN and structural features. The purpose is to enhance the learning and memory of important features of input data, and then form an efficient and accurate shortterm load forecasting model in parallel. The experimental results show that compared with ALSTMs and CNNLSTMs, the prediction error of the optimization method in this paper decreases by 6% and 4% respectively, and the prediction error fluctuation decreases by 4.7% and 4.8% respectively.