Due to the volatility and randomness of photovoltaic power generation, it is difficult for traditional models to accurately predict it. To solve this problem, a prediction model of AW-CNN-LSTM is established based on clustering. First, the photovoltaic power plant historical data set is pre-processed and clustered using the K-means clustering algorithm based on the elbow method; secondly, an adaptive weight is established based on the distance between the training samples and the feature center of test samples of the same clustering category; then, an AW-CNN-LSTM network model suitable for different clustering categories is established based on the clustering results and adaptive weights. CNN are used to capture the relationships between different features, while LSTM are used to capture temporal features. Finally, the forecast results of each model are integrated to get the final forecast results. Experiments on the data set of photovoltaic power stations in the Australian Desert Solar Energy Research Center demonstrate the effectiveness of the proposed method.