The　openness　of　networks　renders　them　vulnerable　to　various　forms　of　attacks.　When　networked　switched　systems　(NSS)　suffer　from　denial　of　service　(DoS)　attacks　and　delays,　the　real-time　property　of　the　dataset　decreases,　greatly　affecting　the　control　performance.　To　address　this　issue,　this　article　proposes　a　switched　adaptive　dynamic　programming　(ADP)　predictive　control　method.　An　event-triggered　mechanism　is　designed　to　reduce　unnecessary　waste　of　network　transmission　resources.　A　predictive　mechanism　is　designed　to　accurately　reconstruct　the　missing　system　state　and　switching　signal　under　DoS　network　attacks.　Then,　the　reconstructed　data　are　applied　to　train　the　actor　and　critic　neural　networks,　which　are　used　to　approximate　the　optimal　control　policy　and　performance　index　function　(PIF)　of　the　NSS,　respectively.　Furthermore,　the　iterative　convergence　of　the　switched　ADP　algorithm　is　proved.　Finally,　a　numerical　example　is　provided　to　verify　the　effectiveness　of　the　proposed　method.