The existing attributed community search methods mainly utilize seed nodes to explore a densely connected subgraph which satisfies both structure cohesiveness and attribute homogeneity. However, in many application scenarios, since the diversity of attribute types and the uncertainty of seed node quality, it is challenging to develop stable community search methods. To solve these problems, we propose a two-stage community search method based on Seed Replacement and joint Random Walk (SRRW). First, we use the overlapping clustering method to cluster nodes. Second, we propose a dynamic local clustering coefficient and cluster center membership coefficient to evaluate the quality of nodes. Third, we propose a seed replacement strategy to replace the seed node with the core member of the target community. Finally, we use joint random walk and parallel conductance value to detect a local community. Experimental results on synthetic and realworld networks show that SRRW is more robust to the seed-dependent problem and the seed-invalid problem than state-of-the-art algorithms.