Aiming at the problems of redundant input characteristic data, poor anti-interference ability and limited predictive accuracy in short-term photovoltaic power prediction, short term forecasting of photovoltaic output based on feature factor selection and IVMD-GLSSVM is proposed. Firstly, GRA-KCC is used to analyze the characteristic factors that affect the photovoltaic power, and extract the extremely relevant characteristic factors that affect the photovoltaic power. Then IVMD is used to decompose the photovoltaic power data to reduce the impact of data nonlinearity and volatility on prediction accuracy. Then each modal component is input into the GLSSVM prediction model for prediction, and the superposition of the prediction results of each subsequence is the final prediction result. Finally, the prediction model and other models are verified and analyzed in MATLAB. The results show that the proposed prediction model has strong anti-interference ability and high prediction accuracy.