Temporal　Lobe　Epilepsy　(TLE)　is　the　most　common　subtype　of　focal　epilepsy　and　the　most　refractory　to　drug　treatment.　Roughly　30%　of　patients　do　not　have　easily　identifiable　structural　abnormalities.　In　other　words,　MRI-negative　TLE　has　normal　MRI　scans　on　visual　inspection.　Thus,　MRI-negative　TLE　is　a　diagnostic　and　therapeutic　challenge.　In　this　study,　we　investigate　the　cortical　morphological　brain　network　to　identify　MRI-negative　TLE.　The　210　cortical　ROIs　based　on　the　Brainnetome　atlas　were　used　to　define　the　network　nodes.　The　least　absolute　shrinkage　and　selection　operator　(LASSO)　algorithm　and　Pearson　correlation　methods　were　used　to　calculate　the　inter-regional　morphometric　features　vector　correlation　respectively.　As　a　result,　two　types　of　networks　were　constructed.　The　topological　characteristics　of　networks　were　calculated　by　graph　theory.　Then　after,　a　two-stage　feature　selection　strategy,　including　a　two-sample　t-test　and　support　vector　machine-based　recursive　feature　elimination　(SVM-RFE),　was　performed　in　feature　selection.　Finally,　classification　with　support　vector　machine　(SVM)　and　leave-one-out　cross-validation　(LOOCV)　was　employed　for　the　training　and　evaluation　of　the　classifiers.　The　performance　of　two　constructed　brain　networks　was　compared　in　MRI-negative　TLE　classification.　The　results　indicated　that　the　LASSO　algorithm　achieved　better　performance　than　the　Pearson　pairwise　correlation　method.　The　LASSO　algorithm　provides　a　robust　method　of　individual　morphological　network　construction　for　distinguishing　patients　with　MRI-negative　TLE　from　normal　controls.