In view of the problems that the existing 3D visual grounding methods rely on expensive sensor equipment， have high system costs， and exhibit poor accuracy and robustness in complex multi-target grounding scenarios， a multi-target 3D visual grounding method based on monocular images was proposed. In this method， natural language descriptions were combined to achieve the recognition of multiple 3D targets in a single RGB image. To this end， a multi-target visual grounding dataset， Mmo3DRefer， was constructed， and a cross-modal matching network， TextVizNet， was designed. In TextVizNet， 3D bounding boxes for targets were generated by a pre-trained monocular detector， and visual and linguistic information was integrated deeply via an information fusion module and an information alignment module， thereby realizing text-guided multi-target 3D detection. Experimental results of comparing with 5 existing advanced methods including CORE-3DVG （Contextual Objects and RElations for 3D Visual Grounding）， 3DVG-Transformer， and Multi3DRefer （Multiple 3D object Referencing dataset and task） show that TextVizNet improves the F1-score， precision， and recall by 8.92%， 8.39%， and 9.57%， respectively， on the Mmo3DRefer dataset compared with the second-best method Multi3DRefer， improving the precision of text-based multi-target grounding in complex scenarios significantly， and providing effective support for practical applications such as autonomous driving and intelligent robotics.