Introduction: Recently, big data attract great attention from industries, academia, and governments. Education big data from teaching and learning processes used to assess elementary education level is scarce in China. Case description: Gansu province, located in the northwest China, is one of the most backward economy areas where the development of elementary English education falls far behind developed areas of the country. Taking Gansu province as a case study and English subject in senior high schools as the study object, we use score data of English academic proficiency test (2018-2020) from 322 senior high schools to explore spatial-temporal differences of English education level among those schools in order to promote the balanced development of regional education. First we briefly describe the generation of English big data from senior high schools, and then, we mine the big data to analyze the difference of the English education level using the anomaly method, the comparison method, the cluster analysis method, and hot point analysis method. English excellence degree is developed as an indicator to assess elementary English education level. Discussion and Evalution: The results show that: (1) there exists obvious difference in English education level among the selected senior high schools: the highest average value of English excellence degree is 759 times the lowest, and the number of schools with negative anomalies accounts for more than 50%; (2) English excellence degree displays an obvious agglomeration characteristic in Lanzhou city, the provincial capital, that is, the hot spots (high English level) of English excellence degree are located in Lanzhou, while the cold spots (low English level) are concentrated in minority and poor areas such as Linxia autonomous region, the major part of Longnan and the eastern part of Qingyang; (3) The investigation in the form of questionnaires confirms the fact that disadvantage schools in English level are usually lack of good teachers who have a preference to teach in areas with good conditions. Conclusion: From the results, we can draw the conclusion that big data play an important role in quantitatively assessing basic education level, which lays foundation for improving the regional education level. Individuals’ value orientation of study, attitude towards study and the study behavior will be investigated in the future.