The goal of reliably outperforming non-learning planners via learning is still to be achieved. A novel structure-oriented learning-based planning method (SOLP) is presented. SOLP anaylyses the structure knowledge, decomposes the planning problem into initial sub-state and goal sub-state, its solution into plan fragment, when planner finds out a solution successfully. The structure knowledge from previous experiment, or prior knowledge, will be saved in domain. When encountering new problem, SOLP firstly recalls the prior problem structure equivalent or similar to the current problem and the corresponding plan fragment from the domain file, then instantiates the learned prior knowledge as ground knowledge, and finally, encodes the ground knowledge as a satisfiability clause. These clauses, together with the set of clauses from the problem, form the input of the algorithm. SOLP calls the SAT Solver to determine the final solution. An experiment is conducted to test the algorithm in several different domains from IPC to demonstrate the efficiency and effectiveness of the new approach. The results show that, the speed of SOLP has obvious advantage than that of non-learning planner, with up to 80% improvement in extreme case.