In order to improve the economic benefits of photovoltaic power generation systems, accurate prediction of photovoltaic power generation revenue has become particularly important. However, traditional prediction algorithms such as CNN and LSTM suffer from overfitting issues, while the latest emerging KAN neural network also has problems with parameter tuning difficulties and sensitivity to data quality. Based on these issues, this paper proposes a CNN_LSTM_KAN photovoltaic power generation revenue prediction algorithm based on genetic algorithm. The experimental results show that the algorithm outperforms traditional methods in both prediction accuracy and robustness, providing an effective solution for predicting the revenue of photovoltaic power generation systems.