Selecting　appropriate　measurement　to　characterize　the　connection　strength　is　of　great　significance　to　construct　epileptic　brain　network　for　the　purpose　of　brain　disease　course　recognition.　In　this　paper,　Kullback-Leibler　(KL)　divergence　is　introduced　as　an　effective　measure　to　describe　the　similarity　between　EEG　channels,　and　the　network　based　on　KL　divergence　is　used　to　identify　the　significant　differences　of　special　channels　and　detect　seizures.　First,　the　KL　divergence　is　calculated　based　on　the　empirical　cumulative　distribution　function,　and　the　inverse　divergence　connecting　matrix　is　constructed　according　to　the　symmetrized　KL　divergence.　Then　the　node　strength,　weighted　clustering　coefficient,　weighted　characteristic　path　length　and　small-world　property　of　morphometric　similarity　brain　network　are　calculated　based　on　the　connecting　matrix.　Finally,　Wilcoxon　signed-rank　significance　test　is　adopted　to　analyze　the　differences　between　the　different　periods　of　epilepsy　for　the　channel　from　different　perspectives.　The　experiment　is　conducted　on　the　original　EEG　signal　and　EEG　signals　under　different　frequency　bands,　and　different　complex　network　measures　are　calculated　respectively.　At　the　significance　level　of　0.05,　it　is　found　that　the　channels　with　significant　differences　during　seizures　are　FP1-F7,　T7-P7,　F4-C4,　FP2-F8　and　P8-O2　for　CHB-MIT　dataset.　In　addition,　the　dynamic　changes　of　node　strength　of　each　channel,　mean　weighted　clustering　coefficient　and　mean　weighted　characteristic　path　length　over　time　are　visualized　to　explore　the　significant　changes　in　the　long　course　of　epileptic　evolution,　and　the　significant　changes　are　found　before　the　onset　of　the　seizure.