Accurately modeling the continuous evolution of user preferences remains a fundamental challenge in sequential recommendation. While existing models have made significant strides in capturing sequential dependencies, they predominantly encode historical behaviors via discrete aggregation, often failing to explicitly model the continuous evolution trends and intrinsic momentum underlying preference shifts. To address this limitation, we propose Kinematic Evolution for Sequential Recommendation (KERec), which models user preference evolution from a continuous kinematic perspective. KERec adopts a dual-branch encoding architecture: the global context branch captures stable long-term preference structures, while the kinematic evolution branch explicitly models the first-order velocity and second-order acceleration of preference evolution based on Taylor series expansion and multi-scale difference operators. To effectively integrate these heterogeneous signals, we introduce an adaptive feature fusion mechanism that balances steady-state consistency with dynamic sensitivity. Furthermore, we design a momentum-guided contrastive learning paradigm that constrains augmentation views via evolutionary momentum, suppressing random noise while reinforcing the perception of genuine evolutionary trajectories. Extensive experiments on multiple public benchmark datasets demonstrate that KERec significantly outperforms state-of-the-art methods in recommendation accuracy and exhibits superior robustness under varying sequence lengths and noisy levels. The code of our method is available at https://github.com/yyt-1219/KERec.