Abstract: In order to improve the performance of existing higher-order community detection algorithms, a higher-order community detection algorithm based on motif-based modularity optimization is proposed. By quantifying the number of motifs as the weight between nodes, the higher-order community detection based on motifs is transformed into lower-order weighted network community detection based on edges, and a weighted modularity optimization problem is constructed. Based on the meta-heuristic algorithm as the optimization strategy, the lower-order topology structure and higher-order weight information are comprehensively utilized to design the neighborhood community modification operation and local search operation of nodes, so as to improve the quality of community partitions and prevent the algorithm from falling into local optimum. Experimental results on synthetic and real-world networks show that the utilization of motifs is helpful to improve the detection performance under the condition of fuzzy community structure. The proposed algorithm can effectively realize motif-based community detection and has certain advantages in accuracy and quality compared with existing typical motif-based algorithms, which helps to deepen the understanding of the higher-order structure and functional characteristics of complex networks.