Abstract: 【Purpose/significance】In the context of big data, the era of data-intensive research has arrived. The evaluation of data liter⁃acy of university graduate students can help understand the current situation and promote the improvement and optimization of data lit⁃eracy education.【Method/process】Based on related researches, this study constructs a data literacy evaluation index system for univer⁃sity graduate students based on data life cycle, including 4 primary indicators and 18 secondary indicators. The data are collected through questionnaires and the evaluation index system is validated by using BP neural network.【Result/conclusion】The results of da⁃ta analysis show that the constructed evaluation index system has certain practicality and can provide reference to the data literacy ed⁃ucation of university graduate students in the context of big data.【Innovation/limitation】The limitations of this study are mainly in two
aspects. First, the sample was selected mainly from masters, which led to the limited representativeness of the evaluation results. Sec⁃ond, the evaluation method is single, and there is no comparison of different evaluation methods.