Due　to　the　influence　of　natural　factors　and　complex　loads,　the　mechanical　performance　of　ancient　brick　structures　is　deteriorating,　and　assessing　the　strength　of　bricks　is　crucial　for　structural　safety.　The　sintered　clay　bricks　of　the　Ming　Dynasty　Beijing　city　wall　were　scanned　by　computed　tomography　(CT),　and　the　grey　images　of　the　microstructure　and　morphology　inside　the　brick　were　obtained.　The　parameters　including　porosity,　large　porosity,　average　sphericity,　graded　sphericity　porosity　greater　than　0.8　and　fractal　dimension　have　the　largest　correlation　to　the　compressive　strength　of　bricks.　These　parameters　are　used　as　input　data　and　the　compressive　strength　is　used　as　output　data,　and　then　trained　with　the　Back-Propagation　(BP)　neural　network.　The　results　show　that　the　BP　neural　network　model　is　more　accurate　than　the　traditional　fitting　model　in　assessing　the　compressive　strength,　which　can　provide　the　basis　and　reference　for　the　rapid　and　non-destructive　acquisition　of　the　strength　indexes　of　ancient　green　bricks.