In order to solve the problem of high cost of wafer test, some quality prediction solutions based on spatial correlation have been proposed in the field of adaptive test. But most of these solutions sacrifice too much forecast accuracy in order to reduce costs. To solve this problem, this paper proposes a fine-grained quality prediction method. This method uses the Bad Neighbor Ratio to classify the grains predicted by the spatial correlation model, and selects different test sets for different types of grains. In addition, a spatial verification step is introduced before the selection of the die test set, which can ensure the test quality of the entire solution. The experimental results show that compared with the indirect test method, the average test escape rate of the proposed method is reduced by 83%, and the average test item saving rate is increased by 14%. Compared with the dynamic part average test method, the average test escape rate is reduced by 81%, and the average test item saving rate is increased by 17%.