Abstract
A data-driven intelligent analysis method is proposed in this paper to explore and identify the enterprise's technological innovation influencing factors. Questionnaire surveys or expert interviews are usually adopted by the traditional evaluation methods for indicators of technological innovation selection. However, it inevitably involves human factors and experts' subjective judgments, which may affect the result of enterprises evaluation. The research presents an improved text clustering method based on a semantic concept model to explore and analyze the key influencing factors of enterprise's technological innovation. The study collects textual data from 400 enterprises in Beijing and smart analyzes the critical influencing factors of enterprise's technological innovation by using the proposed method. The influencing factors can be divided into seven categories. In addition, compared with the traditional K-means clustering method, the proposed method has a good effect. We proposed a methodology to conduct an intelligent analysis for enterprise's technological innovation under the data-driven. It can provide more objective and auxiliary suggestions for the evaluation of the enterprise's technology innovation.
Highlights
Technological innovation is the foundation of the survival and development of enterprises and the driving force for the country’s economic and social development
It is essential for an enterprise to gain a competitive advantage by correctly analyzing and evaluating the technological innovation capability
E improvement to the traditional K-means algorithm proposed in this paper based on semantic similarity and relevance mainly includes (1) using the improved method based on the maximum similarity to determine the initial cluster centre and reduce the random position of the cluster centre. (2) e improved Euclidean distance with semantic similarity and relatedness is used to measure the similarity between cluster centres and sample sets, instead of the traditional K-means algorithm, which ignores the semantic relationship between terms
Summary
Technological innovation is the foundation of the survival and development of enterprises and the driving force for the country’s economic and social development. Is paper collects technical data from 400 enterprises in Beijing It combines the proposed intelligent text clustering algorithm to realize knowledge mining and acquire enterprise’s technological innovation at the semantic level. It can objectively reflect the actual level of enterprise’s technological innovation capabilities by applying big data-driven intelligent analysis methods It can use massive and multidimensional data to establish a more comprehensive evaluation system. (2) e improved Euclidean distance with semantic similarity and relatedness is used to measure the similarity between cluster centres and sample sets, instead of the traditional K-means algorithm, which ignores the semantic relationship between terms. E improvement to the traditional K-means algorithm proposed in this paper based on semantic similarity and relevance mainly includes (1) using the improved method based on the maximum similarity to determine the initial cluster centre and reduce the random position of the cluster centre. (2) e improved Euclidean distance with semantic similarity and relatedness is used to measure the similarity between cluster centres and sample sets, instead of the traditional K-means algorithm, which ignores the semantic relationship between terms. (3) By adding convergence conditions, the original K-means algorithm solved the problem of unstable clustering results
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