As an objective identification method of seasonal division, the multi-factors climate state similarity measurement has been widely used in climate change research, climate monitoring and short-term climate prediction operations in recent years. The key point of the method lies in the fusion of multi factors and the selection of typical fields. In the multi-factors climate state similarity measurement, the typical field is the climate state anomaly field which can represent the winter and summer climate states. There were three different typical attempts in this paper (1) mean climate state typical field for 60 years, (2) mean climate state typical field for 30 years, and (3) mean climate state typical field for every year. Impacts of the typical field's selection on the seasonal division by the multi-factors climate state similarity measurement have been analyzed, then, taking the Central China area in 1998 and 2013 as an example, the accuracy of the seasonal division results of third typical field is discussed. The results showed that, the typical field as the classification index of the multi-factors climate state similarity measurement is crucial to the seasonal division results and climate change research. The differences between the climate state typical field of a single year and multi-year average are interdecadal, and are bigger in the turning period of climate state change. The division results can accurately reflect the changes of the climate state and the regional atmospheric circulation in central China in 1998 and 2013.