With　the　development　of　intelligent　manufacturing　and　＇Industry　4.0＇,　the　traditional　methods　of　material　mechanical　properties　evaluation　cannot　meet　the　needs　of　industrial　production　due　to　the　shortcomings　of　wasted　materials,　tedious　processes,　and　poor　accuracy.　This　paper　combines　artificial　intelligence　technology　to　propose　a　new　material　performance　evaluation　method.　The　laser　additive　manufacturing　is　taken　as　the　research　background,　three　kinds　of　Ti6-Al-4V　material　microstructure　images　with　different　properties　are　used　as　data　sets,　based　on　DenseNet　model,　a　deep　convolution　neural　network　NDenseNet　is　trained　to　optimize　the　network　model　memory　and　improve　the　recognition　accuracy.　The　experimental　results　show　that　the　accuracy　of　the　model　reaches　90.4%,　loss　value　remains　at　25%.　Params　and　FLOPs　are　significantly　reduced　compared　with　DenseNet　model.　It　only　takes　0.1　seconds　to　process　a　microstructural　image　on　a　GPU　processor.　This　method　can　greatly　reduce　the　work　of　researchers,　improve　product　development　efficiency　in　industrial　environment,　reduce　human　errors,　save　production　materials,　and　has　guiding　significance　for　the　development　of　high-performance　materials.　©　2019　IEEE.