There was a large amount of low-quality data in the monitoring data of bridge cable dynamic response. The existing data detection research focused on obviously abnormal data with abnormal time-domain waveforms. However, there was chaotic data in frequency-domain characteristics with normal time-domain waveforms in the monitoring data, which can’t accurately obtain the dynamic characteristics of bridge cables. Aiming at this problem, the existing abnormal data detection was extended to data quality evaluation, and the obvious abnormal data and frequency-domain chaotic data were detected at the same time. The data quality evaluation method of bridge cable dynamic response monitoring was established by using a convolutional neural network (CNN) and data frequency-domain features. The implementation process included: the time-domain data sequence was transformed into a power spectral density function (PSDF) by fast Fourier transform (FFT), the Gramm angular field (GAF) method was used to visualize the PSDF sequence, and a CNN model was designed and built to evaluate the data quality automatically. Taking the cable acceleration monitoring data of a cable-stayed bridge as an example, the applied research was carried out. The results show that compared with the time-domain sequence detection method, the PSDF sequence detection method can better distinguish normal and pseudo-normal data, and has a higher evaluation accuracy rate; The accuracy rate of the CNN model established by using the monitoring data of two sensors to evaluate the quality of all 26 sensor monitoring data is above 94%; In addition, the evaluation model established by this method is applied to the monitoring data quality evaluation of another similar bridge with an accuracy of 95%.