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The purpose of this paper is to explore the correlations among entrepreneurial environment, market-oriented strategies, and entrepreneurial performance.

Entrepreneurial environment is measured by institutional environment and industrial environment. A survey of 176 large Chinese automobile firms is conducted. The structural equation model is applied to perform analysis.

The uncertainty of the institutional environment is positively related with market-oriented strategies and market-oriented strategies are positively related with firms’ performance. The stronger the uncertainty of the industrial environment is, the larger impact that market-oriented strategies have on firms’ performance will be. There is no distinct positive relationship between the uncertainty of industrial environment and firms’ market-oriented strategies. The hypothesis, that the stronger the uncertainty of institutional environments is, the larger the impact that market-oriented strategies will be on firms’ performance, is not supported.

In terms of research design, this paper does not select survey samples randomly. This paper only takes institutional and industrial environments into consideration while the environmental characteristics are omitted.

This paper expands entrepreneurship research by integrating previous studies. Findings in this paper are helpful for firms in emerging countries to implement “going abroad strategies,” to start up new businesses in other countries, and to achieve the goals of improving competitiveness and integrating with international firms.

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.