To　address　the　significantly　increased　heat　dissipation　requirements　of　5G　base　stations,　a　flat　plate　heat　pipe　(FPHP)　of　500　x　200　x　3　mm　3　comprising　of　19　independent　channels　was　developed.　To　solve　the　problem　of　simulation　distortion　due　to　the　high　superheat　of　the　initial　boiling　of　the　working　fluid　in　the　ultra　-long　channel　of　490　x　5　x　1.5　mm　3　,　this　paper　adopts　the　Lee　model　considering　the　superheat,　and　simulates　based　on　the　Volume　of　fluid　method,　and　the　simulation　results　are　compared　with　the　experiments,　and　the　average　relative　error　is　less　than　5　%.　The　FPHP　was　explored　at　100　-　500　W　heat　power　and　filling　ratio　of　30　%-70　%,　depending　on　the　application　scenarios　of　the　base　station.　By　partitioning　the　19　channels　and　calculating　the　thermal　resistance　of　the　FPHP　based,　an　Artificial　neural　network　(ANN)　model　with　10　hidden　layer　nodes　is　established　based　on　675　data　sets.　The　results　showed　that　the　mean　square　error　and　correlation　coefficient　were　1.30　x　10　-5　and　0.9990.　The　ANN　prediction　results　are　validated,　the　mean　relative　error　is　5.4　%,　demonstrating　that　the　ANN　model　can　be　an　effective　tool　to　optimize　the　FPHP　for　the　telecommunication　equipment.