had collected clinical dual-wavelength (630 nm,940 nm) PPG signals from subjects to fit a non-invasive Hb detection model, achieving satisfactory effects [18]. A complex network is a network structure formed by connecting nodes and edges according to certain predefined rules. Many complex problems in nature can be better understood through complex network theory [19]. Connections in a complex network can be directed or undirected, and they can be either weighted or binary, depending on the specific needs [20]. Several kinds of literature are proving that the use of complex network techniques in physiological state detection can be attempted. In 2018, [PERSON], [PERSON], et al. used complex network techniques applied in deep anesthesia assessment with more optimistic results [21]. Subsequently, in 2022, [PERSON], [PERSON], et al. trained and validated a deep learning model for continuous blood pressure prediction using visibility graph (VG) complex network transformations on PPG signals and used its adjacency matrix thermal pictures to achieve accurate predictions [22]. In 2024, [PERSON], [PERSON], et al. extracted complex network features and performed classification model training for [PERSON]'s disease severity after biomarkers of [PERSON]'s disease were transformed using a visibility complex network and achieved high accuracy [23]. In a previous study, [PERSON] et al. [24] used a four-wavelength PPG signal for non-invasive detection of Hb. This study shows that the use of MW-PPG signals is superior to single-wavelength PPG signals for Hb detection and confirms that MW-PPG features correlate with Hb and that the machine learning model XGBoost gives the best fitting results and better model performance. In this study, the application of complex network technology in non-invasive detection technology of Hb is mainly explored in depth, and the MW-PPG signals were transformed into natural visibility graph (NVG) and horizontal visibility graph (HVG) complex networks, respectively [25]. Multi-sample complex networks were generated according to the morphological features of PPG signals. Among them, natural visibility graph (NVG) is a method to map time series into complex networks. It can reveal some important laws and information hidden in the time series. HVG, on the other hand, is a new visibility graph algorithm proposed by [PERSON] et al. [26], which shortens the change time of NVG and simplifies it. Considering that this study aims to explore the relationship between different wavelengths, different samples, and Hb indexes, the weighted undirected graph is chosen to generate the complex network [27]. This complex network contains more information than the binary network and can better explain the implicit relationship between different samples in complex systems. In the signal acquisition device, PPG signals of 660 nm, 730 nm, 850 nm, and 940 nm wavelengths were still selected for acquisition, and, considering the influence of contact pressure on the morphological characteristics of fingertip PPG signals [28], a mechanical structure was designed under the signal receiving end of the acquisition device to control the subjects to maintain the appropriate contact pressure to minimize the influence on the quality of PPG signals. Subsequently, multi-wavelength fingertip PPG signals with invasive test results were collected from 152 volunteers in a hospital. The PPG was generated into natural visibility graph (NVG) and horizontal visibility graph (HVG) complex networks according to the period, and its related features were extracted. Meanwhile, all the samples were generated into a multi-sample weighted undirected graph complex network, its network clustering was obtained, and the related information features of each point in the multi-sample complex network were extracted. Finally, relief feature filtering was used to select the most relevant features, and the filtered features were used for classification and regression model training.