研究生管理系统与人工智能体的融合实践

import pandas as pd

from sklearn.metrics.pairwise import cosine_similarity

# 假设我们有学生和导师的数据

students = pd.DataFrame({

'student_id': [1, 2, 3],

'research_interest': ['AI', 'Data Science', 'Machine Learning']

})

mentors = pd.DataFrame({

'mentor_id': [101, 102, 103],

'research_interest': ['AI', 'Data Science', 'Computer Vision']

})

# 将兴趣转换为向量

from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer().fit(students['research_interest'].append(mentors['research_interest']))

student_vectors = vectorizer.transform(students['research_interest'])

mentor_vectors = vectorizer.transform(mentors['research_interest'])

# 计算相似度

科研管理平台

similarities = cosine_similarity(student_vectors, mentor_vectors)

# 推荐最相似的导师

for i in range(len(students)):

print(f"Student {students.loc[i, 'student_id']} 最推荐的导师是 {mentors.loc[similarities[i].argmax(), 'mentor_id']}")