Goal-conditioned Hierarchical Reinforcement Learning (GCHRL) has demonstrated effectiveness in addressing complicated decision-making tasks by providing ''temporal extraction'', which decomposes tasks into smaller and more manageable ''subgoals''. This enables agents to plan over a longer time scale. However, achieving optimal exploration and exploitation still remains a challenge, especially for long-horizon or sparse-reward scenarios. In this paper, we introduce Active exploraion and hierarchical Self-Imitation (ASI), an effective scheme to enhance exploration and exploitation based on subgoal representation learning. The key point of ASI is to utilize temporal adjacency information in the representation space. We construct and dynamically update an adjacency graph that captures the relationships between subgoals. Based on the adjacency information provided by the graph, we design two mechanisms: (1) active frontier-reaching'' exploration that faster expands the explored area by targeting boundary regions, and (2) hierarchical self-imitation learning that leverages historical experience to facilitate both frontier reaching and policy training. Experimental results show that our method accelerates exploration and outperforms existing baselines in challenging long-horizon continuous control tasks.