(2019) derived the hyperparameters of the random forest (RF) model through the Bayesian optimization algorithm, enabling accurate assessments of the temperature field for the design, construction, and maintenance of large-span bridges. [PERSON] et al. (2019) proposed a temperature time series prediction model, which combined the Seasonal-Trend decomposition based on LOESS (STL) ([PERSON] et al., 2019) decomposition method, Jump-Upward-Shift-and-Trend (JUST) algorithm, and bidirectional long short-term memory (Bi-LSTM) network ([PERSON] et al., 2019), which integrated the advantages of various methods, eliminating the fluctuation influence of temperature time series and making accurate temperature predictions. It can be seen that the comprehensive use of models to improve prediction model accuracy is evident and also provides effective and reasonable ideas for solving our problems. To enhance prediction accuracy, researchers have developed hybrid models that leverage the strengths of individual models. The hybrid prediction model incorporates data preprocessing, optimization algorithms, and ensemble neural networks. [PERSON] [20] introduced the Attention-based Convolutional Bidirectional Long Short-Term Memory (AC-BiLSTM) architecture, which effectively processes sequential data by combining convolutional layers with attention mechanisms to enhance the BiLSTM algorithm. Convolutional layers extract information from the initial data, while the attention mechanism assigns different weights to the BiLSTM hidden layer's output. [PERSON] et al. [21] utilized a Bayesian optimization algorithm to fine-tune the hyperparameters of a random forest (RF) model, enabling precise temperature field assessments for the design, construction, and maintenance of large-span bridges. [PERSON] et al. [22] proposed a temperature time series prediction model that integrates the Seasonal-Trend decomposition based on LOESS (STL) [23], the Jump-Upward-Shift-and-Trend (JUST) algorithm, and a bidirectional long short-term memory (Bi-LSTM) network. This approach combines the strengths of various methods to reduce the fluctuation in temperature time series and deliver accurate predictions. These comprehensive model strategies significantly improve prediction accuracy and offer effective solutions to our challenges. This paper proposes a model for bridge temperature prediction, which combines convolutional networks, attention mechanisms, and Bi-LSTM neural networks, as well as uses the Crested Porcupine Optimizer to optimize the parameters of the network. This model has stronger expressive power and generalization ability in temperature prediction tasks, accurately capturing the regularities of temperature changes and being applicable to various complex temperature transformation scenarios. The attention mechanism was initially used for visual image processing and has since been widely applied to sequence data processing and natural language processing (NLP) [23; 25; 26; 27]. In our data, temperatures often exhibit phased different trends. The model with an attention mechanism can dynamically learn the importance of the input data at each time step, thereby improving the performance and generalization ability of the model. We also use the Crested Porcupine Optimizer (CPO) optimization algorithm to optimize the network model parameters, using the number of LSTM units, Dropout Rate, and Learning Rate as optimization parameters, as well as the model's Loss as the fitness function for iteration to obtain the best parameters. Using the optimized model to predict temperature series can effectively capture the long-term dependencies of the temperature series and generate accurate forecast results. The model can accurately predict the temperature of the main girder, providing important data support for bridge construction and maintenance. Our on-site measurements focus on the vertical temperature distribution of the bridge's main girder and our predictions target these measurement points as well.