Background:　Traditionally,　the　diagnosis　of　intracranial　aneurysms　has　relied　on　the　experience　of　the　doctor　in　assessing　the　scanning　results　of　radiological　imaging　technology,　which　is　subjective　and　inefficient,　and　it　is　also　limited　by　the　physical　strength　and　energy　of　the　doctor.　Purpose:　In　order　to　improve　the　diagnostic　efficiency　of　doctors　and　reduce　the　rate　of　misdiagnosis　and　missed　diagnosis　as　much　as　possible.　Methods:　We　propose　a　3D　segmentation　network,　SMNet,　based　on　the　U-Net　architecture　that　combines　spatial　and　multi-scale　features.　The　network　can　better　solve　the　problem　of　intracranial　aneurysm　segmentation　on　magnetic　resonance　angiography　(MRA)　scanning　sequences.　Specifically,　semantic　information　of　different　dimensions　is　extracted　at　each　stage　of　the　encoder　by　the　multi-scale　feature　extraction　block　(MSE　Block)　and　the　strip　volumetric　pooling　block　(SVP　Block),　respectively.　Then,　after　the　fusion　of　adjacent　scale　features　extracted　by　the　decoder,　the　weight　of　features　is　further　redistributed　by　the　quaternary　spatial　attention　block　(QSA　Block).　While　focusing　on　the　important　features,　the　ability　to　discriminate　different　foregrounds　is　improved.　Results:　Experiments　show　that　the　proposed　three　modules　improve　the　segmentation　performance　to　different　degrees.　Dice　and　MIoU　have　increased　by　16.7%　and　28%　compared　to　the　baseline　in　the　private　dataset,　and　the　results　of　the　Aneurysm　Detection　And　segMentation　(ADAM)　public　dataset　are　0.482　and　0.389,　respectively.　It　has　shown　better　segmentation　quality　than　3D　medical　image　segmentation　mainstream　models.　Conclusion:　Our　model　greatly　improves　the　segmentation　results　of　intracranial　aneurysms　with　MRA　images,　and　makes　a　certain　contribution　to　the　clinical　intervention　of　computer-assisted　diagnosis　and　treatment　in　this　field.