A method of Euclidean distance clustering of centroids using improved information entropy is proposed to complete the point cloud alignment, for the traditional point cloud alignment method is susceptible to noise, outliers and overlap, and solves the shortcomings such as causing low alignment accuracy and low efficiency. First of all, voxel grid down sampling is performed on the two point clouds to accelerate the efficiency of subsequent processing. Different from Euclidean clustering directly using distance clustering, this method computes the feature values of points. By calculating the information entropy based on the feature vectors, a feature tensor is employed for cluster selection. Subsequently, key points representing each cluster are extracted, and the KD-tree algorithm is employed for point pair searching and correspondence. Utilizing the positional information of corresponding point pairs, an initial transformation matrix is estimated, serving as input for precise registration and providing a favorable initial pose for subsequent refinement. Finally, a bidirectional KD-tree-enhanced point-to-plane ICP algorithm is employed for accurate registration. A road point cloud data with a length of about 300 m is selected for the experiment, and compared with the four methods at an overlap of 10%, the results show that the RMSE of the algorithm is 0.074 m and the overall time consumed by the alignment process is 30.256 seconds, which is higher than the four algorithms in terms of accuracy and efficiency of the alignment.