Depression is a common mental health issue, and due to limited medical resources, many patients find it difficult to receive timely diagnosis and treatment. In recent years, automated depression assessment based on multimodal information has gradually become a research focus. However, most studies have only focused on multimodal feature extraction and fusion while ignoring the topic-specific differential characteristics across subjects, which play a crucial role in enhancing assessment performance. To address this, this paper proposes a multimodal depression assessment method based on topic modeling and introduces a text-language joint topic modeling model named the Capturing Topic Contributions Network (CTCNet). The aim of CTCNet is to capture the contributions of different topics to depression assessment and improve the assessment performance. First, to solve the problem of losing key depression information in long sequences, this paper propose a topic modeling-based attention mechanism to evaluate the importance of different topics, guiding the model to focus on key depressive information within topics. Then, to better capture critical depression-related information within each topic, this paper employ a multi-dimensional large-kernel convolution module to extract comprehensive depression features from multiple dimension. Furthermore, a dynamic adaptive decision-making mechanism is introduced in the modality fusion module to selectively emphasize key features for fine-grained fusion. Extensive experiments on the DAIC-WOZ and E-DAIC datasets demonstrate that CTCNet significantly improves depression assessment performance, achieving state-of-the-art results.