The rapid development of autonomous driving necessitates precise multisensor data fusion to accurately perceive the surrounding vehicular environment. Central to this is the precise calibration of LiDAR and camera systems, which forms the basis for effective data integration. Traditional neural networks, used for image feature extraction, often yield incomplete or inaccurate results, thereby undermining the calibration accuracy of LiDAR and camera parameters. Addressing this challenge, we propose a novel method hinged on multidimensional dynamic convolution for the extrinsic calibration of LiDAR and camera systems. Initially, data undergoes random transformations as a preprocessing step, followed by feature extraction through a specialized network based on multidimensional dynamic convolution. This network outputs rotation and translation vectors through feature aggregation mechanism. To guide the learning process, geometric and transformation supervisions are employed. Experimental validation suggests an enhancement in feature extraction capabilities of the neural network, leading to improved extrinsic calibration accuracy. Notably, our method exhibits a 0.7 cm reduction in the average error of translation prediction compared with the leading alternative approaches, substantiating the efficacy of the proposed calibration method.