In order to solve the problem that the least square projection twin support vector clustering (LSPTSVC) algorithm fails to make full use of the potential information among sample neighborhoods and is not practical, this paper proposes an efficient weighted least square projection twin support vector clustering algorithm with neighborhood information. Firstly, the algorithm introduces the concept of relative density to fully extract local similarity information between data points of the same class. Then, the algorithm calculates the relative weight of the point. Finally, in order to better reflect the geometric structure of similar samples, the algorithm calculates the weighted average value of the data points by using the relative weight. These experimental results verify the effectiveness of the algorithm. The results show that the proposed algorithm achieves better clustering accuracy than the existing methods under the similar computational complexity and good clustering performance in the practical application of medical datasets in the real world.