The accuracy of short-term electricity price forecasts is of great significance to the electricity market with a diversified competitive landscape. To improve prediction accuracy and efficiency at the jump and peak points of electricity price, a short-term electricity price prediction method based on ATT-CNN-LSTM is proposed for the implied nonlinear relationship between the electricity price series influenced by the fusion of multiple direct and influencing factors. First, the grey correlation degree analysis method is used to analyze the correlation degree between load factors and electricity prices, and the data with a higher correlation degree is selected as the optimal model input. Secondly, the weight of the input data is adaptively allocated through the attention mechanism (ATT), and the strong and weak feature data are distinguished by the weights. Then, a convolution neural network (CNN) is used to perform secondary feature extraction and dimensionality reduction of the data set to optimize the data input into the long short-term memory (LSTM) network, thereby improving the prediction accuracy and training speed of the LSTM network. The actual measurement data of the Australian electricity market is used for a case study, and the comparison with other mainstream algorithms verifies that the proposed method has higher prediction accuracy and computational efficiency.