In response to the problem that traditional obstacle avoidance algorithms cannot guarantee trajectory similarity and smoothness when processing robot reconstruction trajectories, this paper proposes a robot reconstruction trajectory smooth obstacle avoidance scheme based on dynamic artificial potential field. Firstly, Gaussian mixture model and Gaussian mixture regression are used to model and reconstruct the sampled trajectories. The gravitational potential field of the attractor and the repulsive potential field of the obstacle are constructed separately, and a V-shaped extended potential field is superimposed in space to confine the trajectories within the demonstration area, thereby improving the similarity of the trajectories. On this basis, the dynamic artificial potential field method is adopted to guide the generation of obstacle avoidance trajectories, with trajectory tracking and obstacle avoidance achieved by designing the motion mode of attractors and the repulsive force model of obstacles. Finally, a forward-backward fusion planning strategy based on sigmoid function is proposed, which integrates the latter half of the forward planning trajectory and the latter half of the backward planning trajectory together to further improve the smoothness of the trajectory. To verify the effectiveness of the proposed scheme, simulation experiments were conducted on a human handwritten letter dataset and physical experiments were conducted on the trajectory obstacle avoidance of a six-axis robot. Physical experiments have shown that the average curvature of the obstacle avoidance trajectory generated using this scheme is only 0.035 cm-1, and the average tracking error is only 2.96 cm. Compared with the rapidly-exploring random tree method, these values are reduced by 20.1% and 66.9% respectively, and compared with the dynamic motion element algorithm, they are reduced by 28.5% and 20.8% respectively. This study achieved smooth obstacle avoidance while preserving the shape and features of the reconstructed trajectory. During the obstacle avoidance process, both trajectory similarity and smoothness were taken into account, enabling robots controlled by demonstration learning techniques to be more flexibly applied in complex industrial scenarios.