Abstract: Collective spatial keyword queries play an important role in fields such as spatial databases, location services, intelligent recommendations, and group intelligence perception. The existing collective spatial keyword query methods do not consider the problem of requiring time-distance constrained and cost aware, and cannot meet the query needs of most users under time-distance constrained. Existing research results have significant limitations. To make up for the shortcomings of existing methods, collective spatial keyword query based on time-distance constrained and cost aware (called TDCCA-CoSKQ) is proposed. To address the issue of not being able to include both keyword information and time information in existing indexes, the TDCIR-Tree index is proposed, which combines inverted files and time attribute label files. It can reduce the cost of query calculation. The TDCCA_PP algorithm is proposed to address the issue of subsequent screening of collections that meet query criteria for TDCCA-CoSKQ, including TDCCAPruning1, TDCCAPermutation, and TDCCAPruning2. It can improve the efficiency of keyword queries. The TDC cost function and its corresponding sorting algorithm are proposed. The TDC cost function is composed of distance cost and time cost, which includes independent variable coefficients representing user preference α and β. It can increase users' freedom of choice. Effectively solving the problem of existing cost functions not meeting the collective spatial keyword query based on time-distance constrained and cost aware. Theoretical research and experiments have shown that the proposed method has good efficiency and accuracy.