A Study on the Generation of Aesthetic Imagery of Socialist Core Values among College Students Based on DeepDream Algorithm
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Abstract
DeepDream-based generative models can produce visually rich images, but they often lack semantic expression constraints, leading to visual noise, thematic deviation, and aesthetic inconsistency when used to represent abstract values. This study proposes an integrated aesthetic imagery generation framework for socialist core values by combining semantic mapping, style transfer, and constrained DeepDream optimization. Core value keywords are decomposed into visual feature units such as color distribution, structural morphology, and texture complexity, then embedded into multilayer convolutional feature spaces. Layer selection, channel weighting, total-variation regularization, color-distribution constraints, composition control, and human-machine collaborative adjustment are introduced to improve controllability. Experiments show that structural similarity increases from 0.61 to 0.73, PSNR from 18.42 to 21.87, semantic consistency from 0.58 to 0.81, and aesthetic rating from 3.45 to 4.36. The framework provides a controllable visual-generation method for value communication and is relevant to computational imaging, optical feature propagation, and display-oriented electromagnetic visualization.
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References
T. Zhang and F. Yan, “The Shaping of Youth Values from the Perspective of Algorithm Recommendation: Risks, Challenges and Optimization Strategies,” Research on Socialist Core Values,vol.8, no.5, pp.32-41,2023.
X. Zheng and J. Wang, “An Exploration of Strategies for the External Communication of Socialist Core Values,” Journal of Nanjing University of Chinese Medicine (Social Sciences Edition), vol. 25, no.6, pp.385-392, 2024.
L. Wang and X. Tao, “Algorithms Promote the Development of New Productive Forces: Theoretical Logic and Institutional Guarantee,” Contemporary Economic Research, vol. 361, no. 9, pp. 40-51, 2023.
W. Liu, “The Ideology of Algorithms and the Algorithm of Ideology,” Journal of Shenzhen University (Humanities and Social Sciences Edition), vol. 41, no. 1, pp. 152-160, 2024.
J. Xie and Y. Wu, “The Challenges and Countermeasures of Intelligent Algorithm Recommendation to the Construction of Mainstream Ideological Discourse Power,” Journal of Southwest University (Social Sciences Edition), vol. 50, no. 6, pp. 144-154, 2024.
T. Ou, “Embedding and Governance: An Algorithmic Examination of the Intelligent Dissemination of Mainstream Ideological Discourse,” Journal of Xihua University (Philosophy and Social Sciences Edition),vol.43, no. 6, pp. 98-106, 2024.
J. Jin and Y. Xi, “The technical logic and value realization of online ideological and political education in the era of algorithm recommendation,” Pharmaceutical Education, vol. 41, no. 2, pp. 14-21, 2025.
Z. Chen, “Construction and adjustment of ideological and political education for college students from the perspective of generative artificial intelligence,” Journal of Dialectics of Nature, vol. 46, no. 12, pp. 95-100, 2024.