Aiming at the problems of the existing power load forecasting models such as heavy modeling workload， insufficient spatiotemporal joint representation， and low forecasting accuracy， a Short-Term power Load Forecasting model based on Graph Convolutional Network （GCN） combining Long Short-Term Memory （LSTM） network and Self-attention mechanism （GCNLS-STLF） was proposed. Firstly， original multi-dimensional time series data was transformed into a power load graph containing the correlation between series by using LSTM and self-attention mechanism. Then， the features were extracted from the power load graph by GCN， LSTM and Graph Fourier Transform （GFT）. Finally， a full connection layer was used to reconstruct features， and the residual was used to forecast the power load for multiple times to enhance the expression ability of the original power load data. The short-term power load forecasting experimental results on real historical power load data of power stations in Morocco and Panama showed that compared with Support Vector Machine （SVM）， LSTM， mixed model CNN-LSTM and CNN-LSTM based on attention （CNN-LSTM-attention）， the Mean Absolute Percentage Error （MAPE） of GCNLS-STLF was reduced by 1.94， 0.90， 0.49 and 0.37 percentage points， respectively， on the entire Morocco power load test set； the MAPE of GCNLS-STLF on the Panama power load test dataset decreased by 1.39， 0.94， 0.38 and 0.29 percentage points respectively in March and 1.40， 0.99， 0.35 and 0.28 percentage points respectively in June. Experimental results show that GCNLS-STLF can effectively extract key features of power load， and forecasting effects are satisfactory.