The plasma brightness characteristics derived from the femtosecond laser in the micromachining process can be used as real-time feedback measurement signals. Therefore, it is of great significance to study the brightness characteristics of the spot and construct a high-quality brightness change curve with rich details. In this paper, first, in view of the low signal-to-noise ratio of the spot image and the blurred edge of the target area, the improved wavelet threshold denoising method is used to filter the spot image, and a smooth spot with higher edge contrast can be obtained without changing the brightness level of the spot. Then, the K-means clustering method is used to segment the light spot image, and the halo part that appears as noise is segmented out, and only the area with effective brightness is retained. Finally, the brightness change curve of the light spot sequence image is extracted, and the curve is processed by the method of combining fractal and multi-scale soft threshold filtering, and a high-quality brightness change curve with richly detailed information is obtained. The brightness and fractal dimension are calculated using spot images with different powers. The results show that the brightness of the spot and the complexity of the spot transformation both increase with the increase of the laser processing power.