Objective: This study aims to explore application value of artificial intelligence in identification of nature of lung cancer patients in China, analyze its existing problems and advantages, and provide a basis for future solutions. Methods: We randomly selected lung cancer patients (N=121) who received antitumor therapy in our hospital from October 2017 to October 2021. 100 of them met inclusion criteria of WFO. The diagnosis  and  treatment  plan  of  WFO  is  divided  into  three  categories,  namely:  "recommended",  "considerable"  and  "not  recommended".  When  clinical  oncologists  choose  "recommended"  or  "considerable"  regimens,  they  are  considered  consistent,  and  rest  are  considered inconsistent. Descriptive statistics were performed on case characteristics of all patients using Microsoft Excel, and patients were grouped according  to  their  case  classification,  gender,  age,  and  whether  or  not  to  undergo  surgery,  and  their  consistency  was  analyzed.  SPSS17.0  software was used for statistical analysis, and logistic regression model was used to estimate probability ratio and 95% confidence interval of  above  factors.  Result:  Among  all  cases,  21  cases  were  not  applicable  to  WFO.  Among  100  applicable  cases,  median  patient  age  was  61  years,  70%  were  male  and  30%  were  female.  Among  them,  21%  had  surgery,  and  79%  had  no  surgery.  Small  cell  lung  cancer  patients accounted for 19%, and non-small cell lung cancer patients accounted for 71%. Among all enrolled cases, 85% of diagnosis and treatment plans proposed by WFO were consistent with those of doctors. The consistency of patients with small cell lung cancer (SCLC) was 89.48%; consistency  of  patients  with  non-small  cell  lung  cancer  (NSCLC)  was  83.96%.  According  to  tumor  stage,  83.33%  of  patients  with  stage  II,  83.33%  of  patients  with  stage  III  and  85.94%  of  patients  with  stage  IV  had  same  diagnosis  and  treatment  plan.  According  to  gender  classification, consistency between diagnosis and treatment plan proposed by WFO and that of doctors is 88.57% for male patients and 76.67% for female patients. According to classification of operation or not, consistency between diagnosis and treatment plan proposed by WFO and that of doctors was compared, consistency of patients after surgery was 85.72%, and consistency of patients without surgery was 84.82%. According to age classification, according to diagnosis and treatment plan proposed by WFO, 87.93% of patients aged 60 years or older are consistent  with  doctors,  and  80.95%  of  patients  younger  than  60  years  old  are  consistent  with  our  center.  According  to  classification  of  squamous cell carcinoma and adenocarcinoma, squamous cell carcinoma accounts for 28.40% and adenocarcinoma accounts for 71.60%. The diagnosis and treatment plan proposed by WFO was consistent with doctors in 86.95% of squamous cell carcinoma patients and 82.76% of adenocarcinoma patients. Adenocarcinomas were divided into EGFR mutation, EGFR wild-type and undetected according to gene mutation. The consistency of EGFR mutation was 73.34%, consistency of EGFR wild was 85.71%, and consistency of without genetic testing was 86.66%.Small cell lung cancer is divided into limited stage and extensive stage. 77.78% of patients with limited stage according to WFO's diagnosis and  treatment  plan  are  consistent  with  doctors,  while  rate  of  extensive  stage  is  as  high  as  100%.  According  to  logistic  regression  model  analysis, factors such as lung cancer stage, histological type, gender and age had no effect on consistency. Conclusion: The diagnosis and treatment plan proposed by WFO for enrolled cases is highly consistent with that of clinical oncologists;2. The difference in selection of lung cancer  diagnosis  and  treatment  plans  between  artificial  intelligence  and  clinical  oncologists  may  be  affected  by  factors  such  as  patient  constitution,  drug  availability,  and  medical  insurance  plans  in  East  and  West;3.  If  artificial  intelligence  wants  to  be  applied  quickly  and  comprehensively in China, it is necessary to accelerate its localization and improve its consistency.