Global　ocean　heat　content　(OHC)　is　generally　estimated　using　gridded,　model　and　reanalysis　data;　its　change　is　crucial　to　understanding　climate　anomalies　and　ocean　warming　phenomena.　However,　Argo　gridded　data　have　short　temporal　coverage　(from　2005　to　the　present),　inhibiting　understanding　of　long-term　OHC　variabilities　at　decadal　to　multidecadal　scales.　In　this　study,　we　utilized　multisource　remote　sensing　and　Argo　gridded　data　based　on　the　long　short-term　memory　(LSTM)　neural　network　method,　which　considers　long　temporal　dependence　to　reconstruct　a　new　long　time-series　OHC　dataset　(1993–2020)　and　fill　the　pre-Argo　data　gaps.　Moreover,　we　adopted　a　new　machine　learning　method,　i.e.,　the　Light　Gradient　Boosting　Machine　(LightGBM),　and　applied　the　well-known　Random　Forests　(RFs)　method　for　comparison.　The　model　performance　was　measured　using　determination　coefficients　(R2)　and　root-mean-square　error　(RMSE).　The　results　showed　that　LSTM　can　effectively　improve　the　OHC　prediction　accuracy　compared　with　the　LightGBM　and　RFs　methods,　especially　in　long-term　and　deep-sea　predictions.　The　LSTM-estimated　result　also　outperformed　the　Ocean　Projection　and　Extension　neural　Network　(OPEN)　dataset,　with　an　R2　of　0.9590　and　an　RMSE　of　4.45　×　1019　in　general　in　the　upper　2000　m　for　28　years　(1993–2020).　The　new　reconstructed　dataset　(named　OPEN-LSTM)　correlated　reasonably　well　with　other　validated　products,　showing　consistency　with　similar　time-series　trends　and　spatial　patterns.　The　spatiotemporal　error　distribution　between　the　OPEN-LSTM　and　IAP　datasets　was　smaller　on　the　global　scale,　especially　in　the　Atlantic,　Southern　and　Pacific　Oceans.　The　relative　error　for　OPEN-LSTM　was　the　smallest　for　all　ocean　basins　compared　with　Argo　gridded　data.　The　average　global　warming　trends　are　3.26　×　108　J/m2/decade　for　the　pre-Argo　(1993–2004)　period　and　2.67　×　108　J/m2/decade　for　the　time-series　(1993–2020)　period.　This　study　demonstrates　the　advantages　of　LSTM　in　the　time-series　reconstruction　of　OHC,　and　provides　a　new　dataset　for　a　deeper　understanding　of　ocean　and　climate　events.　©　2021　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.