To address the challenge of short-term fluctuation of large-scale photovoltaic power generations pose a challenge to accurate energy measurement, this paper proposes a new method for run clustering for short-term fluctuation of photovoltaic based on improved Gaussian mixture model. Firstly, the characteristics of short-term fluctuation signals of photovoltaic output are analyzed based on the run theory. Secondly, to address the issue of excessive run and difficulty in extracting typical features in the power generation of photovoltaic, the clustering method based on the improved Gaussian mixture model is adopted to cluster the massive run. Furthermore, a subjective-objective fusion evaluation method for clustering results is proposed. Finally, the simulation results of on-site recorded waveforms from photovoltaic power stations show that, compared with other methods, the proposed method has an improvement in clustering result scores ranging from 1.1% to 61.4% in different aspects. The proposed method can maintain good clustering effects under different noise and outlier levels, with a decrease in the composite index score that is less than that of other algorithms by 0.92% to 18.24%. The proposed method achieves adaptive clustering of the Gaussian mixture model through deep learning technology and the Bayesian information criterion, enhancing its adaptability and stability to noisy and outlier data, and enabling reasonable clustering of run-lengths of photovoltaic power station short-term fluctuation signals.