The inspection and regulation of existing operational charging pile meters face problems such as high cost, low efficiency, heavy workload, and shortage of personnel. Additionally, the current performance analysis accuracy of data-driven electricity metering needs improvement. To solve this problem, a time-slicing method based on convolutional bidirectional long short-term memory networks(C-BiLSTM-TS) is proposed to estimate the measurement uncertainty of electricity meters. Considering the spatiotemporal characteristics of the charging facility operation data collected by smart meters, convolutional networks are employed to extract spatial features between variables. The extracted features are then fed into the BiLSTM networks to capture temporal characteristics of the data. Furthermore, since the operation of electricity meters is influenced by the environment and charging demands, which results in long-term non-stationary states, a time-slice method is used to calculate the measurement uncertainty in real-time within time intervals. The proposed method has been validated in a new energy vehicle charging station and compared with PSO-BPNN, DREM-DRLS, and EKF-LMRLS models. The experimental results show that the proposed method has a significant advantage in predicting the measurement uncertainty of charging pile meters, and three performance evaluation indicators of the model have improved by at least 11.45%.