In　recent　years,　the　rapid　development　of　artificial　intelligence　has　promoted　the　widespread　application　of　convolutional　neural　networks　(CNNs)　in　neuroimaging　analysis.　Although　three-dimensional　(3D)　CNNs　can　utilize　the　spatial　information　in　3D　volumes,　there　are　still　some　challenges　related　to　high-dimensional　features　and　potential　overfitting　issues.　To　overcome　these　problems,　patch-based　CNNs　have　been　used,　which　are　beneficial　for　model　generalization.　However,　it　is　unclear　how　the　choice　of　a　patchwise　sampling　strategy　affects　the　performance　of　the　Alzheimer＇s　Disease　(AD)　classification.　To　this　end,　the　present　work　investigates　the　impact　of　a　patchwise　sampling　strategy　for　3D　CNN　based　AD　classification.　A　3D　framework　cascaded　by　two-stage　subnetworks　was　used　for　AD　classification.　The　patch-level　subnetworks　learned　feature　representations　from　local　image　patches,　and　the　subject-level　subnetwork　combined　discriminative　feature　representations　from　all　patch-level　subnetworks　to　generate　a　classification　score　at　the　subject　level.　Experiments　were　conducted　to　determine　the　effect　of　patch　partitioning　methods,　the　effect　of　patch　size,　and　interactions　between　patch　size　and　training　set　size　for　AD　classification.　With　the　same　data　size　and　identical　network　structure,　the　3D　CNN　model　trained　with　48　x　48　x　48　cubic　image　patches　showed　the　best　performance　in　AD　classification　(ACC　=　89.6%).　The　model　trained　with　hippocampus-centered,　region　of　interest　(ROI)-based　image　patches　showed　suboptimal　performance.　If　the　pathological　features　are　concentrated　only　in　some　regions　affected　by　the　disease,　the　empirically　predefined　ROI　patches　might　be　the　right　choice.　The　better　performance　of　cubic　image　patches　compared　with　cuboidal　image　patches　is　likely　related　to　the　pathological　distribution　of　AD.　The　image　patch　size　and　training　sample　size　together　have　a　complex　influence　on　the　performance　of　the　classification.　The　size　of　the　image　patches　should　be　determined　based　on　the　size　of　the　training　sample　to　compensate　for　noisy　labels　and　the　problem　of　the　curse　of　dimensionality.　The　conclusions　of　the　present　study　can　serve　as　a　reference　for　the　researchers　who　wish　to　develop　a　superior　3D　patch-based　CNN　model　with　an　appropriate　patch　sampling　strategy.