In addressing the issues of suboptimal solutions and poor exploration performance in narrow passages of intelligent car path planning using the Rapidly-exploring Random Tree-Connect (RRT-Connect) algorithm, this paper improves the RRT-Connect algorithm in expansion strategy and path smoothing based on an analysis of the basic principle of the RRT-Connect algorithm. Firstly, in terms of expansion strategy, a probability bias method is introduced to screen random points, and an expansion method based on artificial potential fields is used to shorten paths and reduce computation time. Secondly, regarding path smoothing, a third-order B-spline curve is introduced to optimize the path and generate a smooth path, ensuring that the path meet the dynamic characteristics of intelligent cars. Finally, the superiority of the improved RRT-Connect algorithm is demonstrated through comparative simulation. The results show that in environments with simple obstacles, complex obstacles and narrow paths, the average time and path length of the improved RRT-Connect algorithm are superior to those of the traditional RRT-Connect algorithm.