Novice surgeons perform poorly during robotic surgical teleoperation because of poor consistency, a laparoscopic view that lacks depth information, and low proficiency. To make the novice surgeon perform close to expert operation during teleoperation, this article proposes an auxiliary framework for robotic surgery. A Bayesian statistics-based target prediction algorithm is used to realize the prediction of the operator’s intention, and the information entropy is used to measure the credibility of the predicted target. An adaptive velocity-position hybrid control method based on dynamic bubbles is proposed to realize the efficient performance of surgical tasks by seamlessly switching between slow, precise movements and fast, less precise tasks with no need for frequent grasping and repositioning. Assistance for teleoperation is achieved using virtual fixture force assistance based on task guidance constraints and posture adaptive adjustment based on the predicted target. The proposed framework has been validated on a platform for simulated laparoscopic surgery based on a sigma7 teleoperated primary device and a redundant degree-of-freedom surgical robot secondary device to evaluate its usability and effectiveness. It has also shown its superiority in various metrics in a comprehensive performance comparison with the baseline solution.