基于人工智能的校友管理系统设计与实现

单点登录系统

import pandas as pd

# 加载校友数据

alumni_data = pd.read_csv('alumni.csv')

# 数据清洗

alumni_data.dropna(inplace=True)

alumni_data['graduation_year'] = alumni_data['graduation_year'].astype(int)

# 显示前几行数据

print(alumni_data.head())

]]>

from sklearn.cluster import KMeans

# 特征选择

features = alumni_data[['age', 'job_category']]

# 聚类模型

kmeans = KMeans(n_clusters=5)

alumni_data['cluster'] = kmeans.fit_predict(features)

print(alumni_data[['name', 'cluster']])

]]>

import nltk

from nltk.corpus import stopwords

from nltk.tokenize import word_tokenize

nltk.download('punkt')

nltk.download('stopwords')

def extract_keywords(description):

stop_words = set(stopwords.words('english'))

words = word_tokenize(description)

filtered_words = [word for word in words if word.isalnum() and word not in stop_words]

return filtered_words

# 示例

description = "I am an AI researcher with expertise in machine learning."

print(extract_keywords(description))

]]>

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

# 构建推荐模型

model = Sequential()

model.add(Dense(64, input_dim=5, activation='relu'))

model.add(Dense(32, activation='relu'))

model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型

model.fit(X_train, y_train, epochs=10, batch_size=32)

]]>