College teaching evaluation is an important part of teaching because it not only summarizes the semester's teaching results but also guides the next semester's teaching. However, with the implementation of China's quality-oriented education reform and the arrival of the era of big data (BD), new requirements for college teaching evaluation are being put forward, which also brings many reform conditions. Teaching evaluation in the traditional sense is typically based solely on the course's student scoring data. This method is based on a single data source and thus cannot evaluate the teaching effect comprehensively, objectively or fairly. To address this issue, a teaching evaluation method in colleges and universities operating within the BD environment is proposed. This paper investigates college and university teaching evaluation in the post-BD era. The observed value of the Bartlett sphericity test statistic is 6619.943, and the corresponding probability p is nearly equal to 0. If the significance level an is 0.05, the null hypothesis should be rejected because the probability p is less than the significance level a, indicating that the correlation coefficient matrix cannot be the unit matrix. This also implies that there must be some correlation between the original variables, making factor analysis possible. BD can combine scattered and single evaluation data to create continuous and systematic data. BD can provide a large amount of data support for teaching evaluation, making it more scientific and fair; at the same time, driven by BD, teaching evaluation can return the results of teaching evaluation more quickly.