Star coordinates (SC), a traditional multivariate data visualization technique, loses much information due to oversimple dimension reduction algorithm. And the SC visualization can’t offer the dimension distribution information. Moreover, the manual dimension axis configuration of SC is too complex. To address these problems, the paper proposes the advanced star coordinates (ASC), which uses the diameter instead of the radius as the dimension axis, designs the dimension configuration strategy to optimize the order and the angle of dimension axes, and projects the multi-dimensional object to low dimension visual space through the dimension reduction algorithm. And the dimension reduction process is meaningful to user and the algorithm uses the minimum of the object coordinates variation between the multi-dimensional coordinates and the advanced star coordinates as criterion. Experimental results show that the dimension reduction algorithm is highly efficient and suitable for the aggregation with a great amount of high-dimensional data. The dimension configuration strategy relieves the user’s operation burden greatly and helps them detect the connotative characteristics of the multidimensional information aggregation quickly and exactly. The visualization is easy to understand and can express the dimension distribution information effectively, which is helpful for user to view the multi-dimensional information and to discover the implicit information in knowledge discovery process.