Air quality is closely related to modern environmental impact, carbon emission monitoring, and weather forecasting, playing a crucial role in maintaining ecological balance, addressing climate change, and safeguarding public health. High-quality air quality data is indispensable for accurate air quality prediction tasks, directly influencing urban planning, such as public facility placement and transportation planning. It also significantly affects residents’ travel decisions, such as outdoor sports choices and commuting methods. However, in actual monitoring, uncontrollable factors such as equipment failure, network interruption, and sensor contamination often lead to frequent missing data in air quality measurements. This severely threatens the accuracy of subsequent data analysis, the reliability of environmental monitoring, and the effectiveness of environmental governance. Therefore, air quality imputation has become a central concern in the field of air quality detection. Traditional statistical and machine learning-based imputation methods can address short-term data gaps, but they overlook the intrinsic temporal characteristics of air data, leading to large imputation biases. To address this, this paper proposes an air quality imputation model based on a multi-scale attention convolution network to achieve end-to-end imputation of air quality data. First, we introduce a novel multi-scale fractal method to transform one-dimensional air data into two-dimensional data samples with distinct periodic trends, providing a solid foundation for feature extraction. Second, we construct a carefully designed multi-scale hybrid convolution framework to deeply mine two-dimensional data features. Finally, through extensive experimental validation, we demonstrate that this deep learning-based imputation method effectively addresses air data gaps and achieves higher imputation accuracy than mainstream temporal imputation methods, providing strong technical support for air quality monitoring and management.