With the advancement of science and technology as well as the continual improvement of big data analysis technology, the accuracy of traditional data information classification has declined, making it impossible to assess English ability effectively. A competency evaluation model for college English teaching vacancies is built using this information and the big data architecture. The ability of the big data information model is evaluated and feature information of ability constraints is extracted using the predefined constraint parameter index analysis model. Simultaneously, the K-means clustering algorithm is used to cluster and integrate a series of index parameters of English ability using big data, and the English teaching resource allocation plan is completed in accordance with this, allowing for the scientific evaluation of English teaching ability. The results of the studies show that the clustering method utilized in the context of big data can aid in the evaluation of English competence. In the experiment, four test cycles of English teaching skills were set up, and the effectiveness of the English evaluation techniques described in this paper and two classical cluster evaluation methods were compared and tested. The research shows that using the method described in this paper to evaluate English teaching skills can significantly improve the full utilization of data.

In order to improve the effect of English semantic analysis, under the support of natural language processing, this paper analyzes English syntactic analysis and the word sense strategy of the neutral set and solves the parameters through data training, so as to solve the probability distribution of the maximum entropy model of each order. Moreover, by comparing the prediction probability of the model to the judgment mode with the experimental data, it is found that the first-order maximum entropy model (independent model) is quite different from the data. Therefore, when judging data in English semantics, we cannot only consider the influence of second-order correlations but should also consider higher-order correlations. The research results of the simulation experiment show that the English syntactic analysis and the word sense disambiguation strategy of the neutral set proposed in this paper from the perspective of natural language processing are very effective.