Community detection task is a hotspot in data mining. In recent years, deep learning and graph data have been increasingly diverse and complex, and the task of hierarchical community detection has gradually become a focus of research. The goal of this task is to learn the hierarchical relationship between communities while gathering similar nodes in homogeneous graphs to better understand the graph data structure. The introduction of this relationship poses a higher modeling challenge to community detection algorithms. For this task, some effective heuristic methods have been proposed. However, limited by the simple assumptions of community distribution and discrete optimization learning methods, these methods cannot describe more complex graph data, nor can they be combined with other effective continuous optimization algorithms. To solve this issue, we first attempt to model a complex overlapping hierarchical community structure and propose a simple dual-task optimization model of node embedding and community detection. The relationship of nodes and overlapping hierarchical communities can be flexibly explored through gradient updates. In the learning process, we can also obtain the embedding representations of nodes and communities to apply to rich downstream tasks.