The　computation-intensive　situational　awareness　(SA)　task　of　unmanned　aerial　vehicle　(UAV)　is　greatly　affected　by　its　limited　power　and　computing　capability.　To　solve　this　challenge,　we　consider　the　joint　communication　and　computation　(JCC)　design　for　UAV　network　in　this　paper.　Firstly,　a　multi-objective　optimization　(MOO)　model,　which　can　optimize　UAV　computation　offloading,　transmit　power,　and　local　computation　resources　simultaneously,　is　built　to　minimize　energy　consumption　and　task　execution　delay.　Then,　we　develop　Thompson　sampling　based　double-DQN　(TDDQN)　learning　algorithm　which　allows　the　agent　to　explore　more　deeply　and　effectively,　and　propose　a　joint　optimization　algorithm　that　combines　TDDQN　and　sequential　least　squares　quadratic　programming　(SLSQP)　to　handle　the　MOO　problem.　Finally,　to　enhance　the　training　speed　and　quality,　we　incorporate　federated　learning　(FL)　into　the　presented　joint　optimization　algorithm　and　propose　hierarchical　federated　TDDQN　with　SLSQP　(HF　TDDQN-S)　to　implement　the　JCC　design.　Simulation　results　show　that　the　introduced　HF　TDDQN-S　can　efficiently　learn　the　best　JCC　strategy　and　minimize　the　average　cost　contrasted　with　the　DDQN　with　SLSQP　(DDQN-S)　and　TDDQN　with　SLSPQ　(TDDQN-S)　approach,　and　achieve　the　low　average　delay　SA　with　power　efficient.