Three-dimensional (3D) target tracking is a critical research area in autonomous driving. Most existing methods rely primarily on intersection over union or motion features for data association, whereas the 3D morphological and positional characteristics of the target are typically disregarded, thus resulting in significant matching errors. Hence, we propose a novel 3D target tracking approach that leverages the joint multidimensional features of point clouds. First, an object-embedding matching module developed based on a 3D detection backbone was introduced to extract more discriminative 3D features of the target. Second, a motion prediction module was incorporated, thus enhancing the consistency across frames by utilizing historical trajectory data to forecast the target's future position. Finally, a joint multidimensional affinity matrix was constructed by combining 3D shape features, motion characteristics, and center-point positional information, thereby improving the robustness of trajectory and detection associations. Validation on the public nuScenes dataset demonstrates the superior tracking performance of the proposed method, with a 4.4 percentage points increase in the average multi-object tracking accuracy and a reduction of 322 identity switches, thus significantly mitigating identity switching errors. These results prove the method's enhanced efficacy.