Community search on bipartite graphs aims to find a community closely associated with the query vertex for personalized recommendation, fraud detection, and team formation. During community search, considering both the structural closeness and the homogeneity of attributes of nodes is the key to improving the quality of the output community. Traditional work uses the $(\alpha,\beta)$-core model to guarantee structural cohesion of the nodes (i.e., degree of each upper vertex is at least $\alpha$ and degree of each lower vertex is at least $\beta$). However, it ignores the attributes of nodes, resulting in an average attribute similarity of only about 0.17 for the node pairs in the output community. In this paper, a framework for $(\alpha,\ \beta)$-Attributed Weighted Community Search ($(\alpha,\ \beta)$-AWCS) was proposed. It output a connected subgraph of $G$ containing the query vertex, which satisfies both structurally cohesive (i.e., ($(\alpha,\ \beta){-}$-core) and keyword cohesiveness (i.e., its vertices share common keywords). The framework includes a pruning strategy to strip vertices that do not contain query attributes, thus effectively reducing the search space, and two algorithms improve the attributes cohesiveness of the output community. One of the exact algorithms first obtains a subgraph of attribute cohesion and subsequently keeps the structure cohesive. The other approximate algorithm has higher robustness, which iteratively removes the vertex with the lowest attribute score until the structural cohesion cannot be maintained. We have conducted experiments on real datasets of different sizes. Experiments show that both algorithms can improve the attribute cohesiveness metric by more than 25% compared to the traditional method. Meanwhile, structural cohesion was appropriate.