Existing indoor three-dimensional (3D) object detection is able to detect a limited number of object categories, thus limiting the application on intelligent robotics. Open-vocabulary object detection is able to detect all objects of interest in a given scene without defining object categories, thus solving the shortcomings of indoor 3D object detection. At the same time, the large language model with prior knowledge can significantly improve the performance of visual tasks. However, existing researches on open-vocabulary indoor 3D object detection only focuses on object information and ignores contextual information. The input data for indoor 3D object detection is mainly point cloud, which suffers from sparsity and noise problems. Relying only on the object point cloud can negatively affect the 3D detection results. Contextual information contains scene information, which can complement the object information to promote the recognition on object category. For this reason, this paper proposes an open-vocabulary 3D object detection algorithm based on contextual information assistance. The algorithm integrates contextual information and object information through a large language model, and then performs chain-of-thought reasoning. The proposed algorithm is validated on SUN RGB-D and ScanNetV2 datasets, and the experimental results show the effectiveness of the proposed algorithm.