Taiwan Standard Time [TST]) as training images (inputs) were used to create two convolutional neural network (CNN) models. However, splitting the TEC maps of the three time periods into daily TEC images caused wedge effects. The wedge effects were reduced using a low-pass filter called the Butterworth filter. This resulted in clearer TEC precursors for earthquakes, facilitating the identification of earthquakes of magnitude M w ≥ 5.0 that exhibited associated TEC precursors during three periods, particularly for the Chi-Chi earthquake of September 21, 1999. The results of this study were compared with those of Lin et al. (2001) and Lin (2010) associated with the Chi-Chi earthquake. Simultaneously, two CNN models that were developed were verified to be rational due to the high accuracy of their predictions. These two models were used to verify each other's accuracies and to demonstrate the reliability of the method in this study. Therefore, statistical analysis was not the aim. The final outputs of the two CNN model were defined as similarities. Similarities, which are larger than 0.5, were defined as TEC precursors of earthquakes. TEC precursors described as temporal TEC multi-precursors (TTMPs) by Zoran et al. (2018) were detectable on the 1st, 3rd, and 4th days (that is, on September 17, 18, and 20, 1999, respectively) prior to the Chi-Chi earthquake of September 21, 1999. These results are consistent with those of Liu et al. (2001) and Lin (2010). A TEC precursor on May 13, 2003, (TST) was detectable 2 days prior to the earthquake on May 15, 2003, (TST) with the magnitude (M w) of 5.52. The low standard deviation (STD) and mean square error (MSE) confirm the reliability of both CNN models. Regarding mechanical principles, the TTMPs related to the Chi-Chi earthquake were caused by an electric field (Liu et al., 2001). The cause of the TEC precursor on May 13, 2003, prior to the earthquake on May 15, 2003, was an argument without any corresponding study for comparison.