Knots affect the mechanical properties of solid wood plates.The accurate description of knots in wood plates and the calculation of wood board mechanical properties have been issues with great practical value. Nowadays, machine vision method is used to detect the defects on wood surface, ultrasonic testing is used to determine the existence of defects, and X-ray method can give a full description of solid wood, but the cost is high. The near infrared spectroscopy analysis technology has the characteristics of rich structure, convenient testing and being nondestructive, but the redundancy and nonlinear information in the spectrum affect the precision of the modeling. In this paper, a method of identifying the knots based on the fusion of Isomap and wavelet neural networks is proposed, and the nonlinear dimensionality reduction is completed by Isomap. The nonlinear relation is modelled by the wavelet neural network between the material and the angle of the knot edge, and the shape structure of the knot inside the wood plate is performed by the multi-point angle of the edge. First, the method uses the cone model to express the knot structure proposed by Pablo. Knots are extracted by machine vision method from the image, and their center positions are obtained by calculation. Then, the information of multi-point position about the edges of knots is extracted and processed by the baseline drift and denoising methods. After that, abnormal spectrums are eliminated by combining PCA and mahalanobis distance, the calibration sample sets are divided by K-S, and effective spectral information are extracted through Isomap, which set the dimensionality reduction and adjacent number, and the fast modeling of different spectral dimensions is completed through PLS, and then the ideal spectral feathers are iterated. Finally, wavelet network is used to establish the relationship between the edge spectrum and their inclination angel of the knots, and the 3D status of these knots is realized by Solidworks software. In this experiment, 160 sets of spectral data of 40 knots were collected from Larix gmelinii plates. After measuring the relative spatial position of the upper and lower surfaces of knots, true inclination angles of every point were obtained. The result of the experiment reveals that S-G smoothing and first order derivative can give clear outline in spectral pre-processing and the absorption peak is more obvious. When Isomap method is for dimension reduction, with non-linear dimension reduction number d=12, the nearest neighbor number k=19, the SECV is the minimum and the redundant data of spectrum is eliminated. When wavelet neural network is used to build the model of nonlinear inclination angles of knots, the correlation coefficient is 0.88, the prediction standard deviation is 7.65, and the relative analysis error is 2.14. This method can realize the inversion of knot structure in wood plates, and can provide quantitative analysis means for the prediction of mechanical properties.