Modeling the connection between oil painting creation and university students’ inspiration in the context of “Internet+”

Abstract

Abstract In the Internet era, oil painting creation requires constant observation and learning before it can be inspired, and the inspiration for oil painting creation is like a godsend. This paper constructs a fast Fourier transform convolutional model based on fast Fourier transform and convolutional neural network to study the connection between oil painting creation and university students’ inspiration in the context of “Internet+”. The model was evaluated algorithmically, and a dataset of oil paintings was built to investigate the influence of the hue and brushwork technique on students’ inspiration. From the algorithm evaluation, the algorithm of this paper’s model reduces the floating-point computation from 34.76G to 20.08G than the traditional time-domain convolution algorithm, which reduces the floating-point computation by nearly 15G and greatly improves the running speed of this paper’s model. From the hue examples, the percentage of blue-gray, pale white, and light blue oil paintings are 34.95%, 18.59%, and 10.73%, respectively. Various hues will bring students different emotional expressions and induce their creative inspiration. In terms of brushwork techniques, the average percentages of the six brushwork techniques are 16.53%, 22.21%, 15.68%, 11.73%, 14.69%, and 19.16%, respectively. The fast Fourier transform convolution model established under the background of “Internet+” can effectively analyze the different tones and brushstroke techniques in oil paintings so that students can feel the emotion and vitality of the works in the process of observation and thus promote the generation of inspiration for oil painting creation of college students.