Efficient path planning and decision-making are essential for the unmanned platforms to achieve the autonomous exploration in unknown environments. However, due to the limitations in perception accuracy and computational resources, the current exploration methods often suffer from the low efficiency and incomplete coverage in the structurally complex scenarios. In particular, the redundant paths and sensing blind zones frequently occur in cornered or occluded regions. This is mainly because most planners focus only on the local information gain or the shortest path, which fail to fully exploit environmental structural features and results in the high-cost backtracking and repeated exploration. To address these challenges, this study proposes a hierarchical active path-planning method based on the edge-and-corner regions for unmanned platforms. The proposed framework adopts a two-layer structure consisting of front-end path generation and back-end trajectory optimization to achieve the efficient and continuous global exploration. In the front-end stage, a fast environmental information preprocessing mechanism is introduced by combining with an adaptive viewpoint push-away and small disturbance optimization strategy to ensure the balanced coverage and uniform viewpoint distribution in corner regions. In the back-end stage, a multi-factor cost model with corner constraints integrated with path sequence optimization, B-spline smoothing, and terminal correction is designed to generate the continuous, safe, and executable trajectories. Experimental results demonstrate that, the proposed method reduces the average exploration time by 14.7%~18.2% and shortens the path length by 17.4%~39.7% compared with the two advanced algorithms in the typical corner-rich environments. Meanwhile, it maintains an average coverage rate over 96.6% by effectively balancing the exploration efficiency and path optimization performance, thereby significantly improving the overall exploration quality and planning effectiveness. The comparison with the learning-based method further verifies its stability and adaptability in the structurally complex scenarios. Furthermore, the real-world indoor experiments validate the feasibility and adaptability of proposed algorithm, confirming its potential for the practical deployment in the complex exploration tasks.