Accuracy and efficiency are the key metrics for evaluating the performance of feature selection algorithms. They correspond to the attribute dependence and reduction scale of neighborhood rough sets respectively. Conventional feature selection algorithms often optimize solely based on maximum attribute dependence reduction， overlooking the significance of reduction scale. However， as data feature dimensions increase and category hierarchies emerge， category information becomes complex and structural relationships become chaotic. Traditional attribute dependency calculations fail to effectively utilize category hierarchy information， leading to suboptimal classification performance. In response to this， a mixed hierarchical dependency that considers the relationship between attribute importance and category hierarchy structure is constructed. This treats mixed hierarchical dependency and reduction scale as two independent optimization objectives， and introduces a multi-objective evolutionary algorithm to optimize them independently. This approach improves attribute reduction performance from both the attribute dependency and attribute scale perspectives， resulting in reduction results that meet target constraints. Experimental results demonstrate that the proposed algorithm achieves higher-quality reduction results within target constraints， leading to the improvement of classification accuracy.