Under the influence of rapid developments in artificial intelligence

machine learning

as an important component of it

has significantly enhanced the intelligence

informatization

and automation in many scientific research fields. Geotechnical engineering needs to accurately predict and analyze actual engineering

so efficient and accurate processing and analysis of huge data is the key technical requirement. Machine learning is becoming an important driving force for the development of geotechnical engineering because of its advantages in processing huge amounts of data. To fully understand the progress and effectiveness of machine learning in the field of geotechnical engineering

this paper reviews a large number of relevant literature and uses CiteSpace visualization analysis tools to organize them

deeply exploring the current research status and hot issues

and identifying existing challenges and development bottlenecks. Through literature review

it is found that extensive and in-depth research on machine learning has been carried out in the field of geotechnical engineering. However

the development content and research direction of this field show a certain degree of limitation; although new algorithms have injected new vitality into the field of machine learning

the application of the latest algorithm results in geotechnical engineering is not widespread. In view of this

it is urgent to find ways to resolve its limitations and strive to apply the latest achievements to practical engineering to further promote the intelligent level of geotechnical engineering.