Bougainvillea is an important woody ornamental plant in southern China. There are a large number of varieties, but the classification is relatively chaotic. This study took 133 Bougainvillea varieties introduced and preserved in Yunnan as the research object, and conducted frequency distribution statistical analysis, coefficient of variation calculation, and diversity index calculation for 10 quantitative characters and 41 discrete characters. Q-type clustering analysis was also conducted. The results showed that the 133 Bougainvillea varieties had rich diversity in many characters, and the coefficient of variation of quantitative characters ranged from 19.61%-32.59%, with an average of 26.64%. The coefficient of variation of the total flower stem length, calyx tube width, petiole length, and flower length was the highest, and the diversity index ranged from 1.69-2.09, with an average of 1.92. The diversity index of leaf length, bract width, leaf width, and bract length was the highest. The diversity index of discrete characters ranged from 0.17-2.19. There were 11 discrete characters with H′ values greater than 1, and the diversity index of bract color and calyx tube color was both greater than 2. The diversity was rich. Among the four species of Bougainvillea, the diversity index of Bougainvillea×buttiana was the highest, with the richest diversity, followed by B. peruviana, B. glabra, and B. spectabilis, whose diversity index was the lowest, with less diversity. However, each species has its own advantageous characters. Among the 51 characters, 22 characters showed significant difference among species, which were of great reference value for identifying and classifying the varieties of Bougainvillea. Based on the types of bracts, the number of spines, and the size of leaves, 133 B. varieties could be grouped into four categories: double spiny less, double spiny more, single spiny less leaflet, and single spiny large leaflet. The research results show that the phenotypic characters of the 133 B. varieties tested have high diversity, and the quantitative trait variation types are rich. This study could provide scientific basis for systematic evaluation of B. germplasm resources, variety classification, and germplasm innovation.

China’s forestry informatization has entered the stage of smart forestry，and the new generation of information technology plays an important role in the smart forestry. Based on the connotation and development path of smart forestry, this paper refines the system architecture of smart forestry, expounds the research status of key technologies in smart forestry, such as Internet of Things, 3S and BeiDou Navigation, Cloud Computing, Big Data, Mobile Internet, etc., analyzes its main problems, and discusses its development trend and research focus. It is showed that there are some problems in the research of the key technologies of smart forestry in China, such as incomplete infrastructure, high cost, inconsistent technical standards, weak information interconnection, unstable data quality, data mining not deep enough, insufficient technology integration and lack of professional and technical talents. It is suggested that the research focus of the key technologies in China’s smart forestry in the future will be on four aspects: forestry data mining and deep application, forestry cloud remote sensing big data research, forestry block data research, and the deep integration of forestry internet of things, cloud computing, and mobile internet.