The photovoltaic power output is influenced by the randomness and volatility of weather conditions. To address this, an improved BitCN-LSTM neural network-based short-term photovoltaic power forecasting method is proposed using the KNN-LASSO-PCC approach. First, the KNN method is used to clean the dataset. Then, multi-layer feature selection is applied by combining LASSO and PCC. Next, GRU and Elman neural networks are incorporated into the traditional BitCNLSTM method. Specifically, GRU solves long-term dependency issues and parameter optimization problems, while the Elman network enhances local time-series modeling and memory capacity. Finally, after multi-layer feature selection, global horizontal radiation, diffuse radiation, temperature, and humidity are selected as input variables, and the predicted photovoltaic power output for each time period is selected as the final output. A simulation is conducted for a 1~3 day period with predictions made every 15 minutes. The resulting optimal evaluation metrics are an average absolute error of 9.976 3%, mean squared error of 1.702 9%, and average absolute percentage error of 10.626 7%. The training time and optimal testing time are 181.305 1 s and 0.058 932 s, respectively. Compared to other commonly used short-term photovoltaic forecasting models, the proposed method achieves higher accuracy and faster speed.