Short-term　traffic　flow　prediction　is　an　important　basis　of　intelligent　transportation　systems.　Its　accuracy　directly　affects　the　performance　of　traffic　control　and　induction.　To　improve　prediction　accuracy,　in　this　paper,　a　traffic　flow　prediction　model　is　proposed　by　combining　hierarchical　agglomerative　clustering　(HAC),　standardized　Euclidean　distance　(SED),　and　a　long　short-term　memory　network　(LSTM).　The　proposed　model　is　called　the　CHS-LSTM　model.　In　this　model,　HAC　is　used　to　carry　out　cluster　analysis　on　the　original　traffic　flow　data　sample　set　of　target　detection　sections.　Based　on　the　results　of　cluster　analysis,　traffic　flow　data　are　divided　into　several　categories.　For　different　categories　of　traffic　flow,　SED　is　used　to　calculate　the　spatial　correlation　of　the　road　network　where　the　target　sections　are　located,　and　theKsections　most　relevant　to　the　target　detection　sections　are　used　in　the　construction　of　an　input　data　matrix　for　LSTM.　The　prediction　result　with　the　minimum　root-mean-square　error　is　regarded　as　the　final　prediction　result.　The　electronic　toll　collection　data　of　Taiwan　are　used　as　the　fundamental　data　in　this　paper　to　verify　the　performance　and　effectiveness　of　the　CHS-LSTM　model.　The　experimental　results　indicate　that　the　CHS-LSTM　model　can　effectively　improve　the　prediction　accuracy　of　LSTM.　Moreover,　compared　with　well-known　models　generally　used　for　predicting　the　intensity　of　traffic　flow,　our　proposed　model　also　shows　its　superiority.