Urban green space has a variety of ecological, landscape and social service functions. It is an important component of urban green infrastructure and public service facilities, as well as a key factor in a healthy urban environment. Walking plays an essential role in protecting the environment and promoting health, and promoting the walkability of streets helps to increase social vitality and promote sustainable development. Streets have been proven to surpass parks as the most popular places for walking and physical activity. Therefore, it is extremely critical to quantify the walkability of streets and consider the visual experience of greenery for pedestrians. The Walk Score method is now a commonly used method to measure walkability. The method focuses on measuring the convenient accessibility of walking, such as the spatial layout of stores, parks, and other amenities. However, the environmental aesthetic value of street green spaces influences the quality of the pedestrian environment and promotes the motivation of residents to be active, which is realized through the visual participation of people and is an important indicator of walking comfort. In the context of the big data era, new sources of data are constantly emerging. Street view images may capture the green information in a vertical dimension for the indirect representation of people’s perspective on the ground. Moreover, the green view index (GVI) estimated using Baidu Street View images to represent urban street greening, as an emerging green space evaluation index, providing a new way of thinking to quantify the green environment from a human perspective. The GVI was proposed by Japanese scholars and is defined as the percentage of green pixels in the person’s field of view. In recent years, GVI has been incorporated into governmental terminology and has been highly emphasized in China. Aiming at the problem that the current walkability evaluation rarely considers the walking environment, a comprehensive evaluation of street walkability was made on the basis of big data such as road network, point of interest and Baidu street view images. The street function was characterized by the Walk Score, and the street environment was characterized by the GVI. On the basis of Walk Score, the GVI was introduced to evaluate the walkability index of the street comprehensively. In addition, the number of population activities were extracted using the Baidu heat map. Taking Fuzhou downtown as a case study, the comprehensive evaluation method was compared with the Walk Score evaluation method. Its relationship with population activity was explored, and the variables influencing street walkability were examined. The average score of the comprehensive evaluation of street walkability in the study area is 58, which means that the street walkability in Fuzhou downtown is good. Among the streets of different traffic levels and functional types, the streets with the highest walkability are branch roads and residential streets, respectively. Overall, the positive correlation coefficient between the comprehensive evaluation method and the number of population activities is 0.313, which is 0.018 higher than the Walk Score. However, among the different traffic classes, the correlation between the comprehensive evaluation method and population activity is slightly higher than the Walk Score for both branch and secondary roads, and slightly lower for primary roads. Street environments with different traffic classes have different effects on walkability. In particular, there is a significant positive correlation between the GVI and walkability of branch roads (r=0.135). The configuration of public services, GVI, and vegetation diversity all showed significant correlations with street walkability. When the configuration of public services is difficult to improve in a short period of time, increasing the GVI by planting large trees and increasing the diversity of street vegetation may be a convenient way to enhance walkability. The comprehensive street walkability evaluation method in this paper is feasible and optimizes the walking index method to some extent. The analytical approach is applicable for other cities. It provides a new idea for the quantitative evaluation of street walkability, and has certain guiding significance for the planning of pedestrian system and the improvement and enhancement of street green space environment.