Aspect-based　Sentiment　Analysis　(ABSA)　aims　to　predict　the　sentiment　polarity　towards　a　particular　aspect　in　a　sentence.　Most　of　the　existing　methods　construct　neural　networks　or　fine-tune　the　pre-trained　language　models.　However,　it　is　difficult　to　fully　utilize　the　knowledge　of　language　model　learned　during　pretraining　process,　and　it　usually　performs　worse　in　few-shot　experiment.　Prompt　learning　can　alleviate　the　above　problems　effectively.　Manual　prompt　template　construction　methods　are　adopted　usually　but　it　leads　to　high　costs　and　low　efficiency,　and　there　are　seldom　prompt　templates　applicable　to　ABSA　task.　We　propose　a　method　for　ABSA　prompt　template　construction,　named　Average　Gradient　Search　with　External　Knowledge　Based　on　KNN.　The　vocabulary　list　of　the　pre-trained　language　model　is　built　as　a　KD-Tree,　and　the　KNN　algorithm　is　used　to　search　the　best　prompt　template　on　KD-Tree.　For　the　sentiment　polarity　prediction,　the　best　prompt　template　is　used　to　wrap　the　ABSA　data,　converting　the　classification　task　to　generative　task,　which　enables　the　language　model　to　fully　leverage　the　learned　knowledge　from　the　pre-training　stage.　Moreover,　the　constructed　verbalizer　incorporates　external　knowledge　to　provide　label　words　of　each　class　with　extensive　semantic　coverage　and　lower　subjective　bias.　The　comparison　experimental　results　on　SemEval2014　Restaurant　and　Laptop　datasets　demonstrate　that　the　proposed　method　outperforms　the　existing　SOTA　models.　Further,　the　few-shot　experimental　results　indicate　that,　with　only　a　small　number　of　training　samples,　the　proposed　method　achieves　comparable　or　even　better　performance　than　the　baseline　model　trained　on　the　full　dataset.