Cloud phase is not only an important parameter in meteorological and climatological research but also a key element in cloud parameter inversion. Accurate identification of cloud phase is crucial for weather monitoring and forecasting. Traditional cloud phase recognition methods often rely on threshold setting, which is highly subjective and not very reliable. Therefore, this paper proposes a semi-supervised adaptive possibility C-means algorithm that enhances the processing capability of multi-dimensional data and the robustness of classification through semi-supervised learning combined with an adaptive feature weighting mechanism and regularization techniques. By applying this method to Raman lidar and millimeter-wave cloud radar data, it is possible to accurately classify ice clouds, water-dominated mixed clouds, ice-dominated mixed clouds, and supercooled water clouds. Compared with the algorithm before improvement, the classification accuracy has been significantly increased from 0.699 to 0.967, greatly improving the accuracy of cloud phase classification.