Optimization of journalism communication digital education ecology based on SEM model

Abstract

Abstract The optimization path exploration of the digital education ecology of journalism and communication is to provide more comprehensive talents for journalism and communication careers. This paper explains that the SEM model is composed of two parts: measurement model and structural model, and then derives the principle equations of the SEM model and introduces the path diagram, path coefficients, and effect decomposition of the SEM model. The principle and implementation steps of the partial least squares method are then introduced to extract the principal components using orthogonal decomposition, maximize the covariance among the principal components, and then achieve the purpose of using a few variables to monitor the majority of variables. The PLS algorithm is applied to the SEM model to optimize the objective function, and multiple iterations are used to obtain the minimum residuals for all parameter estimates. Finally, the PLS-SEM model is used to analyze the data on the talent cultivation objectives and cultivation mechanism for optimizing journalism and communication digital education ecology, using the University of Z as an example. The percentages of cross-cultural, cross-media, and cross-professional in talent training objectives are 24.57%, 22.59%, and 28.58%, respectively, and the joint training of universities and enterprises in talent training mechanism increased from 7.34% in 2017 to 58.75% in 2021, which is an increase of 51.41 percentage points. The optimization of the digital education ecology of journalism and communication based on the PLS-SEM model should focus on the joint development of schools and enterprises and pay attention to the all-around curriculum training.