Lane change prediction, encompassing both intention recognition and trajectory forecasting, is essential for the safe operation of autonomous vehicles in mixed-traffic environments. Existing models predominantly follow a data-driven paradigm, learning directly from historical vehicle states through an end-to-end approach. Inspired by the emerging paradigm of enhancing model generalizability through domain knowledge, we propose KnowLCP to explicitly model and integrate driving knowledge into the lane change prediction task. Specifically, we incorporate three types of knowledge: traffic risk awareness to improve intention prediction, vehicle kinematics to ensure the physical feasibility of predicted trajectories, and intention intensity to refine trajectory forecasting. Furthermore, we introduce a novel knowledge injection strategy that enhances mutual information during integration and proves superior to the traditional parallel input mechanism, which simply feeds knowledge features alongside historical states. Extensive experiments on two real-world trajectory datasets demonstrate that KnowLCP achieves average improvements of 8.3-10.3% in intention prediction and 10.1-10.3% in trajectory prediction over the best-performing baselines.