Content　caching　in　the　edge　of　wireless　networks　is　a　promising　technology　to　reduce　the　backhaul　traffic　of　duplicated　data　transmission.　Its　key　issue　lies　in　the　accurate　prediction　of　user　requirements.　Considering　the　pervasive　movement　of　users　in　cellular　networks,　especially　in　small-cell　networks,　we　propose　a　mobility　prediction-based　content　caching　replacement　strategy　in　this　paper.　Note　that　the　impact　of　unevenly　distributed　file　popularity　is　much　larger　in　small　cells　than　in　macro　cells　due　to　their　small　coverage,　where　the　local　file　request　profile　does　not　match　the　global　one.　In　more　detail,　the　user　location　predicted　by　long　short　term　memory　(LSTM)　is　incorporated　into　the　caching　replacement　algorithm　based　on　a　deep　reinforcement　learning　(DRL)　framework.　Simulation　results　show　that　the　mobility　prediction　brings　significant　performance　improvement　in　terms　of　cache　hit　ratio　(CHR)　in　various　movement　scenarios,　especially　for　a　more　regular　movement　pattern　of　users.　Moreover,　the　optimal　CHR　threshold　in　the　proposed　algorithm　is　analytically　derived,　and　the　performance　impact　of　learning　rate　as　well　as　the　storage　size　is　also　well　investigated.　©　2021　IEEE.