Abstract

Academics and businesses have been paying more and more attention to the emotional requirements of people as science, technology, and society have advanced. The market has also proposed new standards for the quick iteration of intelligent cultural and creative items simultaneously. The emotional design of innovative, artistic and creative things can give these products emotional qualities and provide customers with a lovely and enjoyable experience. Both domestically and internationally, there has been a great deal of research. This research investigates the data mining technology-based reinforcement learning algorithm of intelligent cultural and creative product creation. This essay will first direct the industrial design content in the information age, shortly focusing on convenience and comfort, summarising the findings of the study done by both domestic and international clever cultural and traditional industrial design produce original works, and use these research materials as inspiration make use of examples, research the components of intelligent artistic and imaginative in terms of form, substance, and colour, and analyse the factors that affect the connected elements, and then examine the three as a sophisticated cultural and creative system. Aspects of intelligent, creative, and cross-cultural contact, interactive behaviour, interactive material, and design of interactions. Second, develop and use the emergence of cultural intelligence and creativity depending on design components for appearance, From the perspectives of ergonomics and product aesthetics, analyse and research intelligent cultural and creative practises, design corresponding conceptual sketches, screen model and render to obtain plans, evaluate complementary design scheme, and complete the physical construction of the project. The interaction technique and interaction content in this intelligent cultural and creative solution are designed, and an easy-to-use application product is designed based on the design above aspects, all by the development trend of interaction design in the future. According to experiments, more than 90% of users are satisfied with intelligent cultural and creative items created by algorithms.

References

1. Kaur A, Kumar Y. Neighborhood search based improved bat algorithm for data clustering[J]. Applied Intelligence, 2022, 52(9):10541-10575.
2. Aruna E . A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining[J]. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(2):1634-1639.
3. Fukuda S . [Advances in Intelligent Systems and Computing] Advances in Affective and Pleasurable Design Volume 774 || Shanghai Shikumen Cultural and Creative Product Design Based on Design Thinking[J]. 2019, 10.1007/978-3-319-94944-4(Chapter 4):33-40.
4. Corbi A, Santos O C, Burgos D . Intelligent Framework for Learning Physics with Aikido (Martial Art) and Registered Sensors[J]. Sensors, 2019, 19(17):3681-.
5. Solozabal R, Ceberio J, Sanchoyerto A, et al. Virtual Network Function Placement Optimization With Deep Reinforcement Learning[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(2):292-303.
6. Luong P, Gagnon F, Tran L N, et al. Deep Reinforcement Learning Based Resource Allocation in Cooperative UAV-Assisted Wireless Networks[J]. IEEE Transactions on Wireless Communications, 2021, PP(99):1-1.
7. Adolphs L, Hofmann T . LeDeepChef Deep Reinforcement Learning Agent for Families of Text-Based Games[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(5):7342-7349.
8. Racharla S P . Data representation and clustering in Sensor Networks Using Unsupervised Learning Algorithm[J]. Journal of Information and Computational Science, 2020, 9(12):917.
9. Zhang Y, Alturki R, Alyamani H J, et al. Multilabel CNN-Based Hybrid Learning Metric for Pedestrian Reidentification[J]. Mobile Information Systems, 2021, 2021(7):1-7.
10. Kksubai F, Srer E . Relational-grid-world: a novel relational reasoning environment and an agent model for relational information extraction[J]. Turkish Journal of Electrical Engineering and Computer Sciences, 2021, 29(2):1259-1273.
11. Yao X, Nazarian S, Bogdan P . Plasticity-on-Chip Design: Exploiting Self-Similarity for Data Communications[J]. IEEE Transactions on Computers, 2021, PP(99):1-1.
12. Javaheripi M, Samragh M, Koushanfar F . Peeking Into the Black Box: A Tutorial on Automated Design Optimization and Parameter Search[J]. IEEE Solid-State Circuits Magazine, 2020, 11(4):23-28.

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