Two-photon lithography (TPL) is an advanced technique used for additive manufacturing. How to effectively inspect the part quality is one of the challenges of TPL before large-scale industrial application. To produce cured part, the light dosage parameter is limited during the fabrication process, and the limit varies from different application scenarios. By automatic recognition of part quality, engineers can efficiently find light dosage limits and monitor the fabrication process. This paper introduces a visual monitoring-based video Transformer with three-dimensional (3D) shifted window multi-head self-attention for automatically detecting part quality in four typical real scenarios. This framework introduces a multi-head self-attention mechanism to capture global features, thereby integrating spatial and sequential information for part quality recognition. The 3D shifted window mechanism is also applied to introduce the locality similar to convolution and reduce computational complexity. In addition, hierarchical representation is introduced to Transformer architecture, which helps to model high-level information from low-level features. The dataset with four scenarios, which are different in write pattern and photoresist, is used to evaluate the feasibility of the industrialization of this framework. The results show that the proposed method has better performance than the traditional deep learning model in the detection of part quality.