In　order　to　promote　the　construction　of　high　performance　intelligent　seismic　test　platform,　realize　the　intelligent　upgrade　and　optimization　of　shaking　table　control　algorithm,　a　deep　learning　controller　framework　of　shaking　table　based　on　LSTM（Long　and　Short-term　Memory　Network）was　proposed　in　the　paper.　The　feasibility　and　effectiveness　of　LSTM　controller　was　verified　by　training　and　simulating　the　input-output　relationship　of　three-variable　controller.　Considering　the　limitation　that　LSTM　relies　on　complete　and　continuous　trajectories　to　preserve　memory,　a　method　of　processing　real-time　feedback　signal　based　on　LSTM　closed-loop　control　was　proposed,　which　helps　the　LSTM　controller　to　avoid　the　loss　of　past　memory　when　receiving　real-time　feedback　signals　by　storing　the　hidden　layer　state“h”and　the　long-term　memory　state“c”separately.　The　simulation　results　shown　that　the　deep　learning　controller　can　effectively　imitate　the　control　performance　of　the　three-variable　algorithm　and　reproduce　the　time-history　curve　of　seismic　acceleration　wave　through　the　training　method　of　supervised　learning,　which　indicated　that　the　deep　learning　controller　has　enough　potential　in　dealing　with　nonlinear　control　problems.　©　2022　Science　Press.　All　rights　reserved.