Philosophical Research on Enterprise Innovation Ecology Based on a Human–Computer Interaction Mental Model

Author:

Dong Yi1

Affiliation:

1. School of Humanities, Shanghai University of Finance and Economics, Shanghai 200433, China

Abstract

Enterprise innovation ecology plays a vital role in the operation and development of enterprises. It can enable enterprises to have benign and sustainable progress space in action. At the same time, it can also make the innovation ability of enterprises grow unprecedentedly, which is a crucial factor related to the survival and lifeline of enterprises. However, enterprise managers are often too immersed in concept innovation and structural optimization in the actual innovation and ecological construction of enterprises. Still, they do not pay attention to the problems in actual construction. This would cause the actual operation of the system to be restricted by key influencing factors such as environmental protection policies, making it difficult to achieve effective progress. In this regard, the purpose of this paper is to analyze and discuss the enterprise innovation ecological philosophy based on the human–computer interaction mental model. Therefore, this paper comprehensively considers four key factors that affect the construction of enterprise innovation ecology, namely, the promotion of enterprise technology, the competitiveness of enterprise market, the innovation characteristics of enterprises, and the environmental constraints of innovation ecology, and improves and perfects the innovation ecosystem from these factors. Finally, an efficient ecological model of enterprise innovation was designed, and the scientific construction was carried out through the human–computer interaction mental model combining human–computer interaction technology and cognitive psychology. At the same time, the model is applied to the actual enterprise operation, and the naive Bayesian algorithm is adopted to optimize the relevant performance of the algorithm model. The experimental results showed that the highest sample accuracy of the algorithm reaches 92.9%, which greatly improved the operation efficiency of enterprise innovation ecological construction.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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