Target community search aims to search cohesive communities consistent with the user’s preference revealed by query nodes, which is a query-dependent variant of community detection in graph analysis. Most of the existing work either directly expands the target community with user-provided nodes or treats all dimensions of the attribute space as equally important, whereas we argue it is more reasonable to locate high-quality nodes and automatically deduce users’ interests with variation of the relevance for different dimensions of the target community. Towards this end, in this paper, we propose a framework named Adaptive Target Community Search with Sample Expansion (ATCS-SE) for attributed graph, which is able to incorporate user’s preference into community search, thus steering the algorithm to detect more interesting and densely connected communities with high attribute semantic similarity. The algorithm consists of three phases: (i) Augmentation of given prior information: the candidate node path containing a finite sequence of nodes similar to the user-provided query node is established to make up a community backbone; (ii) User preference learning and double view weighting: an adaptive two-level weighting mechanism is designed for community backbone to simultaneously compute weights for both view and variables; and (iii) Refinement of the target community score: the quality score of target communities is defined based on the combination of internal consistency and external separability. Extensive experiments are conducted to show the effectiveness and applied value of the proposed method.