(2017) employed a numerical analysis method based on measured temperature fields to study the effect of temperature on the elevation of the main beam. Meanwhile, under the influence of temperature, the main girder of a cable-stayed bridge will exhibit significant deflection, so timely temperature prediction can better assess the health condition of the bridge. [PERSON] et al. (2018) provided comparative values for bridge assessment by establishing a correlation model between temperature and temperature-induced deflection. The studies mentioned above calculated the effects of temperature on bridge deformation based on the measured temperature data and could not guide the temperature corrections for construction stage elevations in advance. Therefore, using a more accurate method to predict short-term temperature is of great practical value. In the early days, people employed models such as the Autoregressive Integrated Moving Average (ARIMA) to forecast and model time series data ([PERSON] et al., 2018). In recent years, with the development of machine learning technology, more and more researchers have been engaged in the study of temperature prediction models, which essentially involve time series prediction. [PERSON] et al. (2018) established a model for predicting the vertical and lateral temperature gradients of concrete box girders using a BP neural network optimized by a genetic algorithm, achieving good results. [PERSON] et al. (2018) used support vector machines (SVMs) to predict temperature levels at specific locations. Both models exhibited high temperature prediction accuracy in terms of temperature prediction. However, considering the characteristics of time series--time dependency and periodic trends--traditional machine learning models may have certain advantages on specific datasets, but they may not effectively capture time-related information in trend data. Therefore, models better suited for handling time dependency and dynamic features have emerged, such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent unit networks (GRUs) ([PERSON] et al., 2014; [PERSON] et al., 2015; [PERSON] et al., 2016), etc. These models can better capture the time relatedness and long-term dependencies in time series data. According to the research, the long short-term memory (LSTM) network outperforms the autoregressive integrated moving average (ARIMA) in terms of performance ([PERSON] et al., 2016). Meanwhile, researchers compared the performance of the LSTM model with the SVM on time series data and concluded that LSTM outperformed the SVM in all scenarios ([PERSON] et al., 2016). Based on LSTM, [PERSON] et al. proposed an LSTM training method incorporating evolutionary attention in 2019. In temperature prediction, this model can achieve competitive predictive performance ([PERSON] et al., 2016). Based on LSTM, scholars have further proposed a bidirectional long short-term memory network (BiLSTM). Experimental studies have shown that the BiLSTM model provides better predictions than LSTM models, but the BiLSTM model reaches equilibrium much slower than models based on LSTM ([PERSON] et al., 2016). To improve prediction performance, researchers have introduced hybrid models to integrate the strengths of each individual model. The hybrid prediction model combines data preprocessing, optimization algorithms, and ensemble neural networks. [PERSON] (2019) proposed an architecture called Attention-based Convolutional Bidirectional Long Short-Term Memory (AC-BiLSTM) to handle sequential data, which achieved good results. This algorithm combines convolutional layers with attention mechanisms, improving the BiLSTM algorithm. The convolutional layer is used to extract information from the initial data, and the attention mechanism is used to give different weights to the output of the BiLSTM hidden layer. [PERSON] et al.