This paper provides an overview and analysis of the basic theory of deep learning (DL), and specifically, a number of important algorithms were compared and analyzed. The article reviewed and analyzed the main applications of DL methods in forestry including surface quality evaluation of sawn timber, forest resource survey, tree species identification, wood moisture content prediction, the specific application of forestry information text classification, etc. Through comprehensive analysis and review, it was found that: (1) DL method has been widely used in the surface quality evaluation of sawn timber, and the research field mainly uses convolutional neural network (CNN) DL algorithms to carry out research on surface evaluation of sawn timber, and the YOLOv4, YOLOv5m algorithm achieves near real-time target detection and recognition. (2) Establishing a suitable remote sensing image recognition method for forest resources based on DL is a method with great application value in the fields of the future forest resource investigation, statistics of forest vegetation coverage, and monitoring and analysis of plant growth status. (3) The tree species recognition method based on DL effectively avoids the disadvantages of other methods that require image preprocessing for tree images, which leads to cumbersome operation process, low efficiency, and large workload. (4) The DL method provides a quick and efficient prediction method for the prediction of wood moisture content. Moreover, the application of the DL method to the classification of forestry information text provides a new solution to the classification of forestry information text. At the end of the article, a summary of the whole paper is given, and the future development trends of applications of DL to forestry: the field of high-end forestry equipment research, microscopic research in forestry science, and smart forestry are predicted.

本文对深度学习（DL）的基本理论进行了概述和分析，特别是对一些重要的算法进行了比较和分析。文章回顾分析了DL方法在林业中的主要应用，包括锯材表面质量评价、森林资源调查、树种鉴定、木材含水率预测、林业信息文本分类的具体应用等。 ，发现：（1）DL方法在锯材表面质量评价中得到广泛应用，研究领域主要利用卷积神经网络（CNN）DL算法开展锯材表面评价研究， YOLOv4、YOLOv5m算法实现了近实时的目标检测和识别。(2)基于DL建立合适的森林资源遥感图像识别方法，在未来森林资源调查、森林植被覆盖率统计、植物生长状况监测分析等领域具有重要应用价值。(3)基于DL的树种识别方法有效避免了其他方法需要对树木图像进行图像预处理，导致操作过程繁琐、效率低、工作量大的缺点。(4)DL方法为木材含水量的预测提供了一种快速有效的预测方法。此外，DL方法在林业信息文本分类中的应用，为林业信息文本的分类提供了新的解决方案。在文章的最后，