Aiming at the problems of slow convergence speed, low search efficiency and random sampling of twoway fast expanding random tree (RRT-CONNECT) algorithm in complex environment with multiple obstacles, this paper proposes an RRT-CONNECT algorithm based on ellipsoid subset sampling. Firstly, on the basis of traditional RRT-Connect algorithm, combined with target paranoid sampling strategy and the advantage of sampling ellipsoid subset, construct a new sampling method, sampling area for constraint, on this basis to find the optimal path from the starting point to the target point point set, and the path as the initial path, by introducing the path pruning algorithm based on triangle inequality, to continuously optimize paths in the iterative process. A path with low cost and no collision was obtained from the starting point to the target point. Finally, a smooth path with continuous curvature was generated by combining the path optimization with the quintic polynomial difference algorithm, so that the manipulator could reach the target point quickly, accurately and stably along the optimal path. Experimental results show that compared with the original RRT-Connect algorithm, the average planning time efficiency is improved by 30.5%, the average sampling points are reduced by 76.74%, and the average path length is shortened by 13.22%. The algorithm has faster convergence speed, higher search efficiency and more significant path optimization effect in the planning process.