Human　pose　estimation　based　on　monocular　video　has　always　been　the　focus　of　research　in　the　human　computer　interaction　community,　which　suffers　mainly　from　depth　ambiguity　and　self-occlusion　challenges.　While　the　recently　proposed　learning-based　approaches　have　demonstrated　promising　performance,　they　do　not　fully　explore　the　complementarity　of　features.　In　this　paper,　the　authors　propose　a　novel　multi-feature　and　multi-level　fusion　network　(MMF-Net),　which　extracts　and　combines　joint　features,　bone　features　and　trajectory　features　at　multiple　levels　to　estimate　3D　human　pose.　In　MMF-Net,　firstly,　the　bone　length　estimation　module　and　the　trajectory　multi-level　fusion　module　are　used　to　extract　the　geometric　size　information　of　the　human　body　and　multi-level　trajectory　information　of　human　motion,　respectively.　Then,　the　fusion　attention-based　combination　(FABC)　module　is　used　to　extract　multi-level　topological　structure　information　of　the　human　body,　and　effectively　fuse　topological　structure　information,　geometric　size　information　and　trajectory　information.　Extensive　experiments　show　that　MMF-Net　achieves　competitive　results　on　Human3.6M,　HumanEva-I　and　MPI-INF-3DHP　datasets.