In　order　to　make　full　use　of　multi-source　remote　sensing　image　resources　in　the　application　of　remote　sensing　image　change　detection,　a　parallel　computing　for　coarse-grained　computing　data　is　a　very　important　choice.　For　the　large-scale　explosive　growth　of　remote　sensing　image　change　which　is　contained　coherent　speckles　detection　sensor　data,　it’s　the　amount　of　data　calculation　is　large　and　time-consuming.　With　the　development　of　modern　remote　sensing　big　data　and　Artificial　Intelligence　(AI),　however,　it　is　difficult　for　traditional　methods　to　solve　an　efficient　algorithm　for　change　detection　in　synthetic　aperture　radar　application.　In　connection　with　acquiring　SAR　(Polarimetric　Synthetic　Aperture　Radar)　Image　to　accurately　obtain　an　effectively　computing　power.　Looking　for　data　processing　and　task　computing　as　the　basis　for　parallelization,　we　propose　a　parallel　change　detection　method　for　multi-temporal　SAR　images　based　on　wavelet　transform,　and　comprehensively　apply　the　Q-learning　model　of　parallel　computing　with　intelligent　processing.　Firstly,　according　to　the　statistical　features　of　SAR　images　and　the　semantics　of　Convolutional　Neural　Network　Pixel　(CNN-Pixel)　analysis　for　an　efficient　change　detection　method　based　on　Q-Learning　semantic　analysis　and　wavelet　transform　is　proposed.　Secondly,　on　the　basis　of　Pixel-Data　is　achieved　the　accuracy　of　change　detection,　probabilistic　and　statistical　conjugate　multi-distribution　function　features,　which　is　pixels　and　tasks　are　pre-processed,　pixel-space　data　calculation　of　Gaussian　mixture　model　and　conjugate　gradient　transform　task　model　for　parallel　calculation.　Thirdly,　the　change-sequence　data　with　obstacles　at　the　time　series　model　of　Q-learning　in　the　changing　target　object　data　on　which　the　surrounding　path　is　an　obstacle　to　achieving　that　image-pixel　sequence　data　of　conjugate　gradient　calculation　data　and　threshold　iteratively　optimizes　the　feature　extraction　of　the　changing　area,　the　multi-sample　training　set　is　obtained,　and　then　the　Bellman　equation　model　is　established　to　calculate　the　statistical　featu　res　including　the　obstacle　for　SAR.　Image　change　detection.　The　experiments　are　show　that　the　parallel　change　detection　method　and　computing　power　will　achieve　better　results,　and　the　Q-learning　method　of　behavioral　science　is　widely　used　in　remote　sensing　applications　to　solve　the　application　of　local　area　change　detection　in　images.　Enhancement-Learning　platform　is　adopted　to　find　the　best　fitness　of　the　threshold　change　computing　precision　data　and　task　hybrid　parallel　computing　model　about　it.　©　2022　SPIE.