Rapid　scientific　and　technological　development　has　brought　many　innovations　to　electronic　devices,　which　has　greatly　improved　our　daily　lives.　Nowadays,　many　apps　require　the　permission　to　access　user　location　information,　causing　the　concern　on　user　privacy　and　making　it　an　important　task　to　protect　user　trajectory　information.　This　paper　proposes　a　novel　model　called　LSTM-DCGAN　by　integrating　LSTM　(Long　Short-Term　Memory　Network)　with　DCGAN　(Deep　Convolution　Generative　Adversarial　Network).　LSTM-DCGAN　takes　the　advantages　of　LSTM　to　remember　attributes　in　the　trajectory　data　and　the　generator　and　the　discriminator　in　DCGAN　to　generate　and　discriminate　the　trajectories.　The　proposed　model　is　trained　using　real　user　trajectory　data　and　the　experimental　results　are　validated　from　the　perspectives　of　both　effectiveness　and　practicality.　Results　show　that　the　proposed　LSTM-DCGAN　model　outperforms　similar　methods　in　generating　synthesized　trajectories　that　are　similar　to　real　trajectories　in　terms　of　the　temporal　and　the　spatial　characteristics.　In　addition,　various　influencing　factors　are　evaluated　to　investigate　ways　of　further　improving　and　optimizing　the　model.　Overall,　the　proposed　LSTM-DCGAN　model　can　achieve　the　balance　between　the　effectiveness　of　privacy　protection　and　the　practicality　of　user　trajectory　data　and　can　thus　be　applied　to　safeguarding　user　trajectory　information.