Applying Deep Learning in the Training of Communication Design Talents Under University-Industrial Research Collaboration

The purpose of the study was to solve the problem of the mismatching between the supply and demand of the talents that universities provide for society, whose major is communication design. The correlations between social post demand and university cultivation, as well as between social post demand and the demand indexes of enterprises for posts, are explored under the guidance of University-Industrial Research Collaboration. The backpropagation neural network (BPNN) is used, and the advantages of the Seasonal Autoregressive Integrated Moving Average model (SARIMA) model are combined to design the SARIMA-BPNN (SARIMA-BP) model after the relevant parameters are adjusted. Through the experimental analysis, it is found that the error of the root mean square of the designed SARIMA-BP model in post prediction is 7.523 and that of the BPNN model is 16.122. The effect of the prediction model that was designed based on deep learning is smaller than that of the previous model based on the neural network, and it can predict future posts more accurately for colleges and universities. Guided by the University-Industrial Research Collaboration, students will have more practice in the teaching process in response to social needs. University-Industrial Research Collaboration guides the teaching direction for communication design majors and can help to cultivate communication design talents who are competent for the post provided.

Automated summary

The study aimed to address the issue of mismatch between supply and demand of communication design talents provided by universities. By utilizing BPNN and SARIMA models, a SARIMA-BPNN model was developed, which showed more accurate post prediction compared to the BPNN model. University-Industrial Research Collaboration guided teaching direction for communication design majors to better meet societal needs.