For precipitation forecast products with different methods and time, a large number of evaluation results often exist together. At present, we’re still lacking effective measures on how to analyze comprehensively and systematically these results. In this study, the agglomerative hierarchical cluster analysis is introduced to classify and analyze the different evaluation results of different forecast products, based on a grid precipitation forecast dataset of each member of the national forecast technology and method competition of CMA from June to September 2019, the central station guide forecast (SCMOC) of the National Meteorological Center, the seamless analysis and forecasting leading-edge system forecast of Chinese Academy of Meteorological Sciences and objective forecast products of 31 provinces (municipalities and autonomous regions)， the global modelforecast of ECMWF (European Centre for Medium-Range Weather Forecasts) and NCEP (National Centers for Environmental Prediction). The results show that the agglomerative hierarchical clustering results can clearly distinguish their similarities and differences between different forecast products. The different evaluation indicators lead to different clustering results, but the forecast products with high similarity are still divided into a same subclass. The identification effect of four different inter-class similarity measurement methods on categories characteristics was different, and the Ward method was followed by Complete, Average and Single method from clear to fuzzy. In addition, the precipitation prediction ability for different administrative regions and forecast products was different, the accuracy of rain probability forecast in North China and East China was better than that in other regions, and most objective forecasts to rain probability and precipitation relative error were better than model forecast of ECMWF, while they to heavy precipitation were worse than ECMWF model, there are still greater difficulties in interpretation to heavy precipitation forecast.