Recently,　learned　heuristics　have　been　widely　applied　to　solve　combinatorial　optimization　problems　(e.g.,　traveling　salesman　problem　(TSP)).　However,　the　scalability　of　these　learning-based　methods　hinders　the　applications　in　practical　scenarios.　Specifically,　models　pre-trained　on　the　small-scale　data　generalize　poorly　to　large-scale　problems.　Moreover,　learning　the　heuristics　directly　for　large-scale　problems　costs　tremendous　time　and　space.　To　extend　the　scalability　of　learned　heuristics　on　TSP,　we　propose　a　Hierarchical　neural　framework　for　solving　large-scale　traveling　salesman　problems　(HiTSPs)　based　on　a　divide-and-conquer　strategy.　In　particular,　the　HiTSP　framework　first　divides　the　large-scale　problem　into　small-scale　subproblems　by　node　clustering.　Each　subproblem　is　conquered　by　a　modified　pointer　network　learned　from　reinforcement　learning.　The　tour　of　the　original　TSP　is　constructed　by　linking　solutions　of　subproblems　and　optimized　by　a　novel　segmented　local　search　algorithm.　Notably,　the　segmented　local　search　algorithm　leverages　the　node　clustering　information　to　prune　many　unnecessary　operations　and　significantly　reduces　the　complexity　in　theory.　Extensive　experiments　show　that　HiTSP　outperforms　state-of-the-art　learning-based　methods　and　Google　OR-Tools　in　large-scale　cases.　Moreover,　compared　to　the　best　heuristic　algorithms,　HiTSP　has　a　significant　advantage　in　efficiency　for　large-scale　TSP　problems.