There is an issue with incorrect teaching posture, yet integrated learning plays a significant part in dance education technology. When used to dance education technology, the conventional clustering technique produces unsatisfactory results and fails to address the application issue. So, we examine the dance education technology and provide a method based on a better clustering algorithm for dance instruction. To begin mitigating barriers to blended learning, we use pattern recognition theory to identify potential influencing elements, and then we partition the indicators based on how they will be utilized in the classroom. Next, an enhanced clustering method is developed using pattern recognition theory for use in blended learning applications, and finally, the outcomes of these applications are thoroughly examined. Using specific evaluation criteria, the MATLAB simulation results demonstrate that the enhanced clustering algorithm outperforms the conventional clustering method with respect to the time of influencing elements in blended learning and the accuracy of its applications.