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In this paper, we introduce a novel indexing scheme-query context tree (QUC-tree) to facilitate efficient query sensitive music search under different query contexts. Distinguished from the previous approaches, QUC-tree is a balanced multiway tree structure, where each level represents the data space at different dimensionality. Before the tree structure construction, principle component analysis (PCA) is applied for data analysis and transforming the raw composite features into a new feature space sorted by the importance of acoustic features. The PCA transformed data and reduced dimensions in the upper levels can alleviate suffering from dimensionality curse. To accurately mimic human perception, an extension called QUC +-tree is proposed, which further applies multivariate regression and EM based algorithm to estimate the weight of each individual feature. The comprehensive extensive experiments to evaluate the proposed structures against state-of-art techniques based on different datasets. The experimental results demonstrate the superiority of our technique.