diff --git a/LSTM.py b/LSTM.py
new file mode 100644
index 0000000..79bf903
--- /dev/null
+++ b/LSTM.py
@@ -0,0 +1,173 @@
+import os,sys
+os.chdir(sys.path[0])
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+from datasets import EnNews
+
+import tensorflow as tf
+from keras.backend.tensorflow_backend import set_session
+ 
+import numpy as np
+import random as rn
+
+np.random.seed(421)
+rn.seed(12345)
+
+import logging
+
+from keras import regularizers
+from keras.layers import Bidirectional, Dense, Dropout, Embedding, LSTM, TimeDistributed
+
+from keras.models import Sequential, load_model
+
+from datasets import *
+from eval import keras_metrics, metrics
+from nlp import tokenizer as tk
+from utils import info, preprocessing, postprocessing, plots
+
+# 记录配置
+
+logging.basicConfig(
+    format='%(asctime)s\t%(levelname)s\t%(message)s',
+    level=logging.DEBUG)
+
+info.log_versions()
+
+# 全局变量
+
+SAVE_MODEL = False
+MODEL_PATH = "models/bilstm.h5"
+SHOW_PLOTS = True
+
+# 数据集和超参数
+Dataset = EnNews
+
+rootpath = "/home/zhangxj/WorkFile/本科毕业设计"
+
+tokenizer = tk.tokenizers.nltk
+DATASET_FOLDER = rootpath+"/EnergyNews"
+MAX_DOCUMENT_LENGTH = 400
+MAX_VOCABULARY_SIZE = 20000
+EMBEDDINGS_SIZE = 50
+batch_size = 32
+epochs = 20
+KP_WEIGHT = 10
+STEM_MODE = metrics.stemMode.both
+STEM_TEST = False
+
+
+
+# 加载数据集
+logging.info("Loading dataset...")
+
+data = Dataset(DATASET_FOLDER)
+
+train_doc_str, train_answer_str = data.load_train()
+test_doc_str, test_answer_str = data.load_test()
+val_doc_str, val_answer_str = data.load_validation()
+
+train_doc, train_answer = tk.tokenize_set(train_doc_str, train_answer_str, tokenizer)
+test_doc, test_answer = tk.tokenize_set(test_doc_str, test_answer_str, tokenizer)
+val_doc, val_answer = tk.tokenize_set(val_doc_str, val_answer_str, tokenizer)
+
+# 完整性检查
+
+logging.info("Dataset loaded. Preprocessing data...")
+
+train_x, train_y, test_x, test_y, val_x, val_y, embedding_matrix = preprocessing. \
+    prepare_sequential(train_doc, train_answer, test_doc, test_answer, val_doc, val_answer,
+                       max_document_length=MAX_DOCUMENT_LENGTH,
+                       max_vocabulary_size=MAX_VOCABULARY_SIZE,
+                       embeddings_size=EMBEDDINGS_SIZE,
+                       stem_test=STEM_TEST)
+
+# 权重训练示例：所有不是 kp的内容
+from sklearn.utils import class_weight
+
+train_y_weights = np.argmax(train_y, axis=2)
+train_y_weights = np.reshape(class_weight.compute_sample_weight('balanced', train_y_weights.flatten()),
+                             np.shape(train_y_weights))
+
+logging.info("数据预处理完成")
+logging.info("可能的最大召回率: %s",
+             metrics.recall(test_answer,
+                            postprocessing.get_words(test_doc, postprocessing.undo_sequential(test_y)),
+                            STEM_MODE))
+
+if not SAVE_MODEL or not os.path.isfile(MODEL_PATH):
+
+    logging.debug("建立网络...")
+    model = Sequential()
+    print("-------",np.shape(embedding_matrix)[0])
+    embedding_layer = Embedding(np.shape(embedding_matrix)[0],
+                                EMBEDDINGS_SIZE,
+                                weights=[embedding_matrix],
+                                input_length=MAX_DOCUMENT_LENGTH,
+                                trainable=False)
+
+    model.add(embedding_layer)
+    model.add(LSTM(300, activation='tanh', recurrent_activation='hard_sigmoid', return_sequences=True))
+    model.add(Dropout(0.25))
+    model.add(TimeDistributed(Dense(150, activation='relu', kernel_regularizer=regularizers.l2(0.01))))
+    model.add(Dropout(0.25))
+    model.add(TimeDistributed(Dense(2, activation='softmax')))
+
+    logging.info("编译网络...")
+    model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'],
+                  sample_weight_mode="temporal")
+    print(model.summary())
+
+    metrics_callback = keras_metrics.MetricsCallback(val_x, val_y)
+
+    logging.info("拟合网络...")
+
+    history = model.fit(train_x, train_y,
+                        validation_data=(val_x, val_y),
+                        epochs=epochs,
+                        batch_size=batch_size,
+                        sample_weight=train_y_weights,
+                        callbacks=[metrics_callback])
+
+    if SHOW_PLOTS:
+        plots.plot_accuracy(history)
+        plots.plot_loss(history)
+        plots.plot_prf(metrics_callback)
+
+    if SAVE_MODEL:
+        model.save(MODEL_PATH)
+        logging.info("模型保存路径 in %s", MODEL_PATH)
+
+else:
+    logging.info("加载模型 %s...", MODEL_PATH)
+    model = load_model(MODEL_PATH)
+    logging.info("加载模型完成")
+
+logging.info("在测试集上预测...")
+output = model.predict(x=test_x, verbose=1)
+logging.debug("输出格式: %s", np.shape(output))
+
+obtained_tokens = postprocessing.undo_sequential(output)
+obtained_words = postprocessing.get_words(test_doc, obtained_tokens)
+
+precision = metrics.precision(test_answer, obtained_words,STEM_MODE)
+recall = metrics.recall(test_answer, obtained_words,STEM_MODE)
+f1 = metrics.f1(precision, recall)
+
+print("###    获得的分数    ###")
+print("###")
+print("### Precision : %.4f" % precision)
+print("### Recall    : %.4f" % recall)
+print("### F1        : %.4f" % f1)
+print("###                       ###")
+
+keras_precision = keras_metrics.keras_precision(test_y, output)
+keras_recall = keras_metrics.keras_recall(test_y, output)
+keras_f1 = keras_metrics.keras_f1(test_y, output)
+
+print("###    获得的分数    ###")
+print("###")
+print("### Precision : %.4f" % keras_precision)
+print("### Recall    : %.4f" % keras_recall)
+print("### F1        : %.4f" % keras_f1)
+print("###                       ###")
+