Contrast-enhanced　ultrasound　(CEUS)　is　a　promising　imaging　modality　in　predicting　the　efficacy　of　neoadjuvant　chemotherapy　for　pancreatic　cancer,　a　tumor　with　high　mortality.　In　this　study,　we　proposed　a　deep-learning-based　strategy　for　analyzing　CEUS　videos　to　predict　the　prognosis　of　pancreatic　cancer　neoadjuvant　chemotherapy.　Pre-trained　convolutional　neural　network　(CNN)　models　were　used　for　binary　classification　of　the　chemotherapy　as　effective　or　ineffective,　with　CEUS　videos　collected　before　chemotherapy　as　the　model　input,　and　with　the　efficacy　after　chemotherapy　as　the　reference　standard.　We　proposed　two　deep　learning　models.　The　first　CNN　model　used　videos　of　ultrasound　(US)　and　CEUS　(US+CEUS),　while　the　second　CNN　model　only　used　videos　of　selected　regions　of　interest　(ROIs)　within　CEUS　(CEUS-ROI).　A　total　of　38　patients　with　strict　restriction　of　clinical　factors　were　enrolled,　with　76　original　CEUS　videos　collected.　After　data　augmentation,　760　and　720　videos　were　included　for　the　two　CNN　models,　respectively.　Seventy-six-fold　and　72-fold　cross-validations　were　performed　to　validate　the　classification　performance　of　the　two　CNN　models.　The　areas　under　the　curve　were　0.892　and　0.908　for　the　two　models.　The　accuracy,　recall,　precision　and　F1　score　were　0.829,　0.759,　0.786,　and　0.772　for　the　first　model.　Those　were　0.864,　0.930,　0.866,　and　0.897　for　the　second　model.　A　total　of　38.2%　and　40.3%　of　the　original　videos　could　be　clearly　distinguished　by　the　deep　learning　models　when　the　naked　eye　made　an　inaccurate　classification.　This　study　is　the　first　to　demonstrate　the　feasibility　and　potential　of　deep　learning　models　based　on　pre-chemotherapy　CEUS　videos　in　predicting　the　efficacy　of　neoadjuvant　chemotherapy　for　pancreas　cancer.