Accurate　prediction　of　pump　characteristics　is　pivotal　for　optimizing　transportation　schemes　and　device　operations　in　long-distance　petroleum　pipelines.　In　response　to　the　variable　nature　of　pump　characteristics　due　to　production　circumstances,　a　novel　dynamic　prediction　model　of　pump　pressure　lift　is　proposed　by　coupling　the　neural　network,　the　intelligent　evolutionary　strategy,　and　an　adaptive　method　of　a　neural　network　coefficient　matrix.　This　model　enables　parameter　adjustments　to　align　with　optimal　directions　through　intelligent　optimization　strategies　in　which　the　differential　evolution　performs　better　than　the　genetic　algorithm　and　particle　swarm　optimization.　The　proposed　model　exhibits　superior　accuracy　and　computational　efficiency　compared　with　classical　static　and　dynamic　prediction　models.　Notably,　the　model　achieves　a　prediction　error　reduction　of　1.34%,　surpassing　existing　dynamic　models　while　offering　an　87.93%　computation　time　reduction　when　contrasted　with　the　static　model