02270nas a2200361   4500000000100000008004100001653001900042653002900061653002600090653002600116653002600142653001600168653001600184653002900200653001500229653002900244653003100273653002500304653002200329653003100351653002200382653003300404653003400437653003100471653002000502653001800522100001000540245011000550856015700660300001200817490000700829520107200836                                       d10aArts computing10aCommunication efficiency10aComputer aided design10aComputer aided design10aComputer-aided design10aDigital art10aDigital art10aEfficient communications10aEmployment10aExpression communication10aImage Processing Algorithm10aImage classification10aImage enhancement10aImage processing algorithm10aImages processing10aIntangible cultural heritage10aIntangible cultural heritages10aNeural networks algorithms10aNoise abatement10aOptimisations1 aN. Li00aDigital Art Creation and Optimization of Intangible Cultural Heritage Based on Image Processing Algorithm  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85178364124&doi=10.14733%2fcadaps.2024.S13.119-134&partnerID=40&md5=4e7aa44cb2e81ec681cf64725e46f5fd  a119-1340 v213 aAs a new art form, digital art provides new ideas and ways for the inheritance and growth of (ICH) culture with its diversified expressions and efficient communication efficiency. In this study, wavelet neural network (WNN) algorithm will be used for image processing. Through feature detection, noise reduction and enhancement of ICH images, clearer and more vivid digital works of art will be obtained. By combining computer aided design (CAD) with traditional handicrafts, it is expected to provide new creative for ICH digital art creation. To verify the rationality and superiority of the image processing algorithm adopted in this article in ICH digital art creation, a computer simulation experiment was conducted. Using WNN s image classification algorithm, the classification accuracy of ICH images of folk dance, traditional handicrafts and folk activities all exceeds 90\%, which proves that the algorithm has excellent classification ability and generalization performance, and can be effectively applied to ICH image classification and recognition tasks.