Accurate　and　fast　spatial　temporal　trajectory　similarity　measure　is　the　foundation　of　spatial　temporal　trajectory　data　mining.　Massive　data,　spatial　temporal　heterogeneous　distribution　and　data　noise　bring　great　challenges　for　spatial　temporal　trajectory　similarity　comparison.　With　the　idea　of　similar　image　matching　in　computer　vision,　we　propose　a　fast　computational　method　TISM-CAE　for　trajectory　image　structure　matching　based　on　convolutional　auto-encoder.　Firstly,　we　remap　the　given　spatial　temporal　trajectory　slices　into　a　two-dimensional　matrix　for　generating　trajectory　images.　Secondly,　we　use　a　convolutional　auto-encoder　network　to　obtain　the　low-dimensional　features　of　trajectory　images　by　unsupervised　learning.　Finally,　the　trajectory　similarity　is　equivalent　to　compare　the　Euclidean　distance　between　two　low-dimensional　features.　We　use　real　floating　vehicle　dataset　of　Shanghai　and　artificial　simulated　trajectory　dataset　for　experimental　analysis.　Final　results　show　that　the　proposed　method　improves　the　accuracy　of　similar　trajectory　identification　and　reduces　the　time　complexity　by　nearly　50%　compared　with　the　currently　used　similarity　measure　method　as　Longest　Common　Subsequence(LCSS)　and　Edit　Distance　on　Real　sequence(EDR),　providing　a　new　direction　for　trajectory　similarity　measure.　©　2022　ACM.