Feature　matching　aims　to　identify　reliable　correspondences　between　two　sets　of　given　initial　feature　points,　which　is　of　considerable　importance　to　photogrammetry　and　computer　vision.　In　this　study,　we　propose　an　innovative　sparse　semantic　learning-based　network,　named　SSL-Net,　for　feature　matching.　Specifically,　SSL-Net　includes　a　novel　sparsity　constraint　(SC)　block,　which　builds　a　sparse　graph　for　sparse　semantic　learning.　The　SC　block　adopts　a　region-to-whole　learning　strategy　to　measure　the　confidence　of　nodes　in　the　sparse　graph.　It　helps　the　sparse　graph　preserve　the　semantic　information　of　positive　influence　while　rejecting　unnecessary　ones,　thereby　suppressing　the　negative　influence　of　incorrect　correspondences.　In　addition,　SSL-Net　also　includes　a　channel-spatial　attention　feature　gathering　block,　which　gathers　features　along　the　spatial　direction　and　channel　dimension　of　correspondences.　To　mitigate　the　existence　of　label　ambiguity,　we　incorporate　the　accommodation　factor　into　the　loss　function　of　SSL-Net　for　feature　matching.　As　a　result,　our　network　outperforms　the　state-of-the-art　method　by　a　considerable　margin.　Notably,　SSL-Net　achieves　a　9.05%　improvement　under　an　error　threshold　of　5　degrees　over　the　state-of-the-art　method　for　the　relative　pose　estimation　task　on　the　YFCC100M　dataset.　Our　code　will　be　available　at　https://github.com/guobaoxiao/SSL-Net.