To address the problem that significant performance degradation of Transformer-based object detection methods when facing long-tailed distributions， a collaborative query optimization-based single-stage end-to-end long-tailed object detection framework， named CQ-DETR （Collaborative Query optimization-based DEtection TRansformer）， was proposed. In the framework， a Layer-Adaptive Encoder Fusion （LAEF） module was designed to integrate multi-scale encoder features dynamically， so as to consider both high-level semantics and low-level details； a Feature-aware Query Generation （FQG） module was designed to generate content-aware queries dynamically from image features， thereby enhancing the representation capability of initial content queries for potential objects； a Category-Localization Joint-aware query Selection （CLJS） mechanism was proposed to achieve collaborative optimization of category coverage and localization accuracy. Experimental results indicate that CQ-DETR is superior to RichSem （Rich Semantics） method on the long-tailed object detection benchmark dataset LVIS v1.0， with the Average Precision （AP） and the AP of rare categories （AP_r） improved by 1.4 and 1.5 percentage points， respectively， verifying the effectiveness of the proposed framework in category-imbalanced scenarios； meanwhile， on the relatively balanced COCO 2017 dataset， compared with DINO （DETR with Improved deNoising anchOr box）， CQ-DETR has the AP improved by 1.1 percentage points， verifying the good generalization ability of this framework in general object detection scenarios.