Wind farms are usually located in high-latitude areas, which bring a high risk of icing. Traditional methods of anti-blade-icing are limited by extra costs and potential damages to the original mechanical structure. Model-based methods are heavily dependent on mathematical models of the blade icing, which are prone to produce erroneous estimation. As data-driven models are better able to achieve competitive performances for the blade icing estimation, this article proposes a temporal attention-based convolutional neural network (TACNN). This novel data-driven model introduces a temporal attention module into a convolutional neural network, with the goal of determining the importance of sensors and timesteps and automatically identifying discriminative features from raw sensor data. Benchmark experiments on ten public datasets of multivariate time-series classification show competitive performance against the state-of-the-art methods. Compared with ten baseline networks and three widely used attention mechanisms, the TACNN shows significant advantages applying to three real-world datasets. These datasets are logged by the supervisory control and data acquisition system and contain operational and environmental measurements such as power and temperature. The ablation study and sensitivity study demonstrate the effectiveness of the key components of the TACNN. The practicability of the TACNN is further verified through online estimation testing.