In the context of the rapid development of economy and culture, people’s requirements for material and culture are constantly increasing, and the relationship between graphic design and human life is also increasingly close. With the continuous development of Internet of Things technology, information exchange between people and things can be realized by using various sensing devices, and innovative modern art can be established. Therefore, the focus of culture has gradually shifted to the field of visualization. However, in the visual communication design, the product design method of “mainly two-dimensional plan and supplemented by three-dimensional model”, on the one hand, cannot maximize the intuitive effect of three-dimensional modeling, thus affecting the efficiency of design; on the other hand, problems such as information incoordination and resource waste are likely to occur. In short, people’s requirements for design have changed from a two-dimensional plane space to a three-dimensional space, and a two-dimensional plane design can no longer meet people’s growing demand for artistic design. Aiming at tackling these problems, this paper proposed to establish a visual communication system based on artificial intelligence (AI). Through this method, the image can be made clearer and has a larger field of view and magnification. At the same time, the system was applied to modern art design, which is a type of innovation. The experimental results showed that the maximum distortion of the system designed in this paper was approximately 15%, and the maximum distortion of the traditional sample was about 20%. Compared with conventional samples, this system has great advantages in graphics transformation. In addition, the chromatic aberration of the optical system can be corrected to improve the imaging effect.

With the improvement of peoples material requirements, the demand for lighting materials is higher and higher. This study mainly discusses the application of the laser Raman spectroscopy of ferroelectric nanomaterial BaTiO₃ in the design of color spotlight products on the stage. Firstly, the effects of different electrospinning parameters on BaTiO₃ nanofibers were studied. Then, the samples were spun under the best spinning parameters and annealed at 600°C, 650°C, 700°C, and 750°C, and then the phase structure, morphology, and piezoelectric properties of the samples at four different annealing temperatures were characterized. In this study, the laser Raman signal converges to the incident slit of the monochromator through the lens. After grating splitting, the signal is obtained through CCD. Finally, the spectral curve is recorded by the computer-controlled data acquisition software winspec/32 and saved as an .spe file. The spectral data to be processed need to be smoothed. The luminescence intensity of BaTiO₃ with flower structure is weak, which is mainly caused by the different crystal forms of the matrix. By analyzing the content of each element, it is found that BA/Ti=1/2, and it can be inferred that the proportion of BaTiO₃ and TiO₂ is about 6. The XRD spectra of the samples obtained at different temperatures (25, 0, 30, 50, and 70°C) corresponded to the standard card BaTiO₃ one-to-one, and no impurity peaks were found, indicating that the obtained samples are pure phases. This research will promote the application of nanomaterials in color spotlight products.

In the context of intensified market competition, consumer demand for aesthetically pleasing and functionally designed products has grown significantly. Color matching in product appearance plays a critical role in influencing consumer choice. However, designers often face challenges related to low color recognition accuracy, which hampers efficiency and design quality. This study explores the application of artificial intelligence (AI) technology to assist in the color matching process of product appearance and functional design. Experimental evaluation across various product types demonstrates that AI integration improves color accuracy by 2.09% and enhances the stability of image representation by 3.47%. Additionally, it reduces design analysis time, increases designer productivity, and boosts satisfaction scores by 5.4%. The findings confirm that AI technology effectively supports designers in achieving more accurate, efficient, and satisfactory color matching outcomes.