Background　This　study　employs　a　network　analysis　approach　to　explore　the　interconnections　between　anxiety,　depression,　and　sleep　problems　and　smartphone　addiction　among　college　students　using　network　analysis,　offering　a　new　perspective　on　these　prevalent　mental　health　issues.Methods　A　cross-sectional　study　was　conducted　among　art　students　at　a　public　university　in　the　province　of　Fujian,　China.　Data　were　collected　using　the　Generalized　Anxiety　Disorder　Scale-7,　Patient　Health　Questionnaire-9,　Pittsburgh　Sleep　Quality　Index,　and　Mobile　Phone　Addiction　Index.　The　R　package　was　used　in　the　analysis　for　statistical　analysis,　and　information　was　collected　using　multi-stage　sampling　as　well　as　stratified　sampling.　Network　analysis　was　utilized　to　identify　bivariate　associations　between　symptoms,　core　components,　co-occurring　patterns,　and　key　nodes　within　the　network.　Network　stability　and　accuracy　were　assessed　using　the　bootstrap　method,　and　network　comparisons　were　conducted　across　subgroups　based　on　gender,　residential　condition,　and　sibling　status.Results　The　study　included　2,057　participants.　The　network　analysis　revealed　uncontrollable　worry　as　the　most　central　symptom,　with　low　energy　and　excessive　worry　also　identified　as　key　symptoms　within　the　network.　Bridge　symptoms　such　as　daytime　dysfunction,　self-harm　or　suicidal　ideation,　abnormal　behavior　and　speech,　and　sensory　fear　were　found　to　be　critical　in　linking　anxiety,　depression,　and　sleep　problems.　The　network　of　comorbid　symptoms　and　smartphone　addiction　highlighted　inefficiency　and　loss　of　control　as　central　factors　influencing　mental　health.　No　significant　differences　in　network　characteristics　were　found　across　the　subgroups,　suggesting　the　universality　of　the　identified　network　structure.Conclusion　This　study　delineates　the　intricate　network　of　anxiety,　depression,　sleep　problems,　and　smartphone　addiction　among　college　students,　identifying　key　symptomatic　intersections　and　their　implications　for　mental　health.