Laser cladding technology is an promising development direction of advanced manufacturing.With the rapid develop-ment of technology and industry,the Quality of the cladding layer is becoming more and more demanding.The characteristics ofthe melt pool,as an important test object in the laser cladding process,directly determine the Quality of the cladding layer.currently the eXtraction of melt pool profiles is based on traditional image segmentation,which does not perform well when en-countering melt pools with obscure features.Therefore,this work developed a molten pool morphology eXtraction method based on semantic segmentation network.The semantic segmentation network is used instead of traditional image segmenta-tion,combined with machine language to achieve high-precision eXtraction of melt pool shape and eXperimental verification to prove the feasibility of the method,laying a solid foundation for the subseQuent detection and analysis of the melt pool.