The spatial co-location pattern is a subset of spatial features whose instances frequently appear together in the neighborhoods. Co-location pattern mining methods usually assume that spatial instances are independent to each other， adopt a participation rate， which is the frequency of spatial instances participating in pattern instances， to measure the importance of spatial features in the co-location pattern， and adopt a participation index， which is the minimal participation rate of spatial features， to measure the interest of patterns. These methods neglect some important relationships between spatial features. Therefore， the co-location pattern with dominant feature was proposed to reveal the dominant relationship between spatial features. The existing method for mining co-location pattern with dominant feature is based on the traditional co-location pattern mining and its clique instance model. However， the clique instance model may neglect the non-clique dominant relationship between spatial features. Motivated by the above， the dominant feature mining of spatial sub-prevalent co-location patterns was studied based on the star instance model to better reveal the dominant relationship between spatial features and mine more valuable co-location patterns with dominant feature. Firstly， two metrics to measure feature’s dominance were defined. Secondly， an effective algorithm for mining co-location pattern with dominant feature was designed. Finally， the experimental results on both synthetic and real datasets show that the proposed mining algorithm is efficient and the co-location pattern with dominant feature is pratical.