Nowadays,　image　recognition　and　classification　methods　are　widely　used　in　various　industries　such　as　automatic　driving,　face　recognitions.　However,　the　classification　of　constructions　is　rarely　studied　in　the　past.　In　recent　years,　Image　recognition　and　classification　are　mainly　based　on　Convolutional　neural　networks　(CNNs)　which　is　one　of　the　representative　algorithms　of　deep　learning.　CNNs　can　greatly　reduce　the　error　rate　of　image　classification.　Therefore,　we　proposed　a　self-defined　CNN　to　do　image　recognition　and　classification　for　some　images　of　buildings,　roads,　bridges　and　so　on.　We　collected　pictures　from　Google　image　manually　as　our　dataset.　For　our　model,　we　first　constructed　the　convolutional　layer　with　filter　size　of　32　kernels,　and　then　created　a　pooling　layer　to　extract　features.　We　repeated　this　process　two　more　time　and　gradually　adding　the　number　of　kernels　to　reduce　sampling　and　the　value　of　training.　In　the　end,　we　constructed　a　fully-connected　layer.　Lastly,　our　method　can　achieve　0.2034　in　loss,　0.9065　in　accuracy,　0.8675　in　validation　loss　and　0.7059　in　validation　accuracy,　which　is　a　relatively　high　level　and　acceptable　in　many　real　applications.　©　2022　IEEE.