Semiconductor yield prediction is essential to control product cost and improve product quality. Defect is the key factor leading to yield loss. Therefore, considering defect gathered characteristics and the nested structure, a yield prediction methodology was presented based on density clustering and hierarchical logit regression. A density clustering algorithm was used to obtain the pattern classification of die defects, and on-line defect data were integrated at the die level as input parameters of hierarchical logit regression model. According to the hierarchical wafer structure, nested variables were added to the model to construct random intercept effect model in lot, wafer and group layers. A non-random intercept and slope model was constructed at the product layer. Finally, according to the regression results, the main factors causing yield loss were analyzed and the corresponding improvement measures were put forward. The simulation experiment shows that the prediction accuracy of the hierarchical logit regression model is better than that of Seed's yield model and nested structure logit model, and has higher prediction ability and feasibility.