Power system management and operation rely heavily on short-term power load forecasting. Accurate forecasting results can help reduce power waste and economic losses. The existing power forecasting methods only forecast the future load based on historical data, which factors have the greatest influence on the power load is not considered enough, and there are no effective methods for simultaneously mining time characteristics and correlation characteristics of multidimensional time series. Therefore, we propose a new hybrid approach, which combines LSTM with attention mechanism and GA (genetic algorithm). In LSTM, GA optimizes the number of layers, dense layers, hidden layer neurons, and dense layer neurons, so as to determine the optimal parameters. On the basis of the load data set containing five characteristics of dry bulb temperature, dew point temperature, wet bulb temperature, humidity and electricity price, the method proposed in this paper will be verified. By comparing with RNN, LSTM, GRU, LSTM-Attention and GRU-Attention. According to the experimental results, the application of the proposed method noticeably minimizes the prediction error and elevates the goodness of fit of the model.